--------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/haselswerdtj/Box Sync/Immigrants & Social Welfare/Replicatio
> n/immigsocwef_main_rep.log
  log type:  text
 opened on:   9 Jun 2020, 16:02:08

. //program:  immigsocwef_main_rep.do
. //task:    Replication of main analyses
. //project:  Social Welfare Attitudes and Immigrants as a Target Population: Ex
> perimental Evidence (Perspectives on Politics)
. //author: Jake Haselswerdt \ 2020-6-9
. 
. //program setup
. version 14

. clear all

. set linesize 80

. macro drop _all

. set scheme s1mono

. set more off

. 
. use immigsocwef_main.dta, clear
(Cleaned survey experiment data; immigration social welfare perception experimen
> t)

. global controls "black hispanic otherrace male ideo01 pid7 agecat incomecat ed
> uc notbornus parents_notbornus sr2k"

. 
. //ASSUMPTIONS
. 
. *H1&2 - main experimental effects
. prtest benefit_immigonly if econtreat==0, by(culttreat) /*H1 - cultural threat
> */

Two-sample test of proportions                     0: Number of obs =      740
                                                   1: Number of obs =      741
------------------------------------------------------------------------------
       Group |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           0 |   .1905405   .0144369                      .1622446    .2188364
           1 |   .1956815    .014574                      .1671169    .2242461
-------------+----------------------------------------------------------------
        diff |   -.005141   .0205141                     -.0453479    .0350659
             |  under Ho:   .0205147    -0.25   0.802
------------------------------------------------------------------------------
        diff = prop(0) - prop(1)                                  z =  -0.2506
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0.4011         Pr(|Z| > |z|) = 0.8021          Pr(Z > z) = 0.5989

. prtest benefit_immigonly if culttreat==0, by(econtreat) /*H2 - fiscal threat*/

Two-sample test of proportions                     0: Number of obs =      740
                                                   1: Number of obs =      728
------------------------------------------------------------------------------
       Group |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           0 |   .1905405   .0144369                      .1622446    .2188364
           1 |   .2596154   .0162491                      .2277678     .291463
-------------+----------------------------------------------------------------
        diff |  -.0690748   .0217361                     -.1116768   -.0264729
             |  under Ho:   .0217913    -3.17   0.002
------------------------------------------------------------------------------
        diff = prop(0) - prop(1)                                  z =  -3.1698
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0.0008         Pr(|Z| > |z|) = 0.0015          Pr(Z > z) = 0.9992

. collapse (mean) prop_immigonly=benefit_immigonly (sebinomial) se_immigonly=ben
> efit_immigonly, by(treatgroup)

. serrbar prop_immigonly se_immigonly treatgroup, scale(1.64) ylabel(0(.05).3) y
> title("Proportion assuming policy will benefit immigrants only") xlabel(1(1)3,
> valuelabel) xtitle("") xscale(r(.5 3.5)) scheme(s1mono)

. graph export Figure2.tif, as(tif) replace
(file Figure2.tif written in TIFF format)

. use immigsocwef_main.dta, clear
(Cleaned survey experiment data; immigration social welfare perception experimen
> t)

. 
. logit benefit_immigonly i.treatgroup, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1149.6406  
Iteration 1:   log pseudolikelihood = -1143.3191  
Iteration 2:   log pseudolikelihood = -1143.2944  
Iteration 3:   log pseudolikelihood = -1143.2944  

Logistic regression                             Number of obs     =      2,208
                                                Wald chi2(2)      =      12.10
                                                Prob > chi2       =     0.0024
Log pseudolikelihood = -1143.2944               Pseudo R2         =     0.0055

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .0329948   .1445252     0.23   0.819    -.2502694    .3162589
Fiscal thr..  |   .4003902   .1313875     3.05   0.002     .1428753     .657905
              |
        _cons |  -1.446502   .0831616   -17.39   0.000    -1.609495   -1.283508
-------------------------------------------------------------------------------

. est sto h12

. 
. *Figure 3 - heterogeneous effects by race
. collapse (mean) prop_immigonly=benefit_immigonly (sebinomial) se_immigonly=ben
> efit_immigonly, by(treatgroup white)

. drop if white==.
(3 observations deleted)

. replace treatgroup=treatgroup+.05 if white==1 
(3 real changes made)

. gen uci=prop_immigonly+1.64*se_immigonly

. gen lci=prop_immigonly-1.64*se_immigonly

. twoway scatter prop_immigonly treatgroup if white==0, ylabel(0(.05).3) ytitle(
> "Proportion assuming policy will benefit immigrants only") xlabel(1(1)3,valuel
> abel) xtitle("") xscale(r(.5 3.5)) || rcap uci lci treatgroup if white==0 || s
> catter prop_immigonly treatgroup if white==1 || rcap uci lci treatgroup if whi
> te==1, legend(order(3 "White" 1 "Nonwhite") ring(0) pos(10) cols(1)) scheme(s1
> mono)

. graph export Figure3.tif, as(tif) replace
(file Figure3.tif written in TIFF format)

. use immigsocwef_main.dta, clear
(Cleaned survey experiment data; immigration social welfare perception experimen
> t)

. 
. *H3a&b - anti-immigration scale interaction
. logit benefit_immigonly i.treatgroup##c.immigscale, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1107.5312  
Iteration 1:   log pseudolikelihood =  -1093.091  
Iteration 2:   log pseudolikelihood = -1092.8852  
Iteration 3:   log pseudolikelihood = -1092.8852  

Logistic regression                             Number of obs     =      2,125
                                                Wald chi2(5)      =      30.97
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1092.8852               Pseudo R2         =     0.0132

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .0248965   .3131058     0.08   0.937    -.5887795    .6385726
Fiscal thr..  |   .3751985   .2835701     1.32   0.186    -.1805888    .9309857
              |
   immigscale |   .8961474    .385204     2.33   0.020     .1411615    1.651133
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |  -.0362369   .4925311    -0.07   0.941     -1.00158    .9291063
Fiscal thr..  |     .03845   .5524646     0.07   0.945    -1.044361    1.121261
              |
        _cons |  -1.900217   .2089378    -9.09   0.000    -2.309727   -1.490706
-------------------------------------------------------------------------------

. est sto h3ab

. logit benefit_immigonly i.treatgroup##c.immigscale ziphisppct2017s $controls, 
> vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1068.3668  
Iteration 1:   log pseudolikelihood = -1006.8962  
Iteration 2:   log pseudolikelihood = -1005.1316  
Iteration 3:   log pseudolikelihood = -1005.1291  
Iteration 4:   log pseudolikelihood = -1005.1291  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(18)     =     236.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1005.1291               Pseudo R2         =     0.0592

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.0396681   .3268144    -0.12   0.903    -.6802125    .6008763
Fiscal thr..  |   .4241517   .3220001     1.32   0.188     -.206957     1.05526
              |
   immigscale |   .2407902   .4992178     0.48   0.630    -.7376587    1.219239
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |   .0566537    .539572     0.10   0.916    -1.000888    1.114195
Fiscal thr..  |    .003957   .6423762     0.01   0.995    -1.255077    1.262991
              |
ziphisppct2~s |  -.0600702   .0455671    -1.32   0.187      -.14938    .0292397
        black |  -.0297215   .2534875    -0.12   0.907    -.5265479    .4671049
     hispanic |   .0149811   .1487716     0.10   0.920    -.2766058    .3065679
    otherrace |  -.4051366   .2139866    -1.89   0.058    -.8245425    .0142694
         male |   .1086036   .1067831     1.02   0.309    -.1006873    .3178946
       ideo01 |   .6844488   .3015901     2.27   0.023     .0933432    1.275554
         pid7 |   .0540763   .0385374     1.40   0.161    -.0214557    .1296082
       agecat |   .1202516   .0731412     1.64   0.100    -.0231025    .2636056
    incomecat |   .0747748   .0228854     3.27   0.001     .0299203    .1196294
         educ |   .1647691   .0552825     2.98   0.003     .0564174    .2731208
    notbornus |   .3356914   .4299746     0.78   0.435    -.5070434    1.178426
parents_not~s |     .23676   .1916579     1.24   0.217    -.1388826    .6124025
         sr2k |   .4346032   .3105186     1.40   0.162     -.174002    1.043208
        _cons |  -3.603906     .30897   -11.66   0.000    -4.209477   -2.998336
-------------------------------------------------------------------------------

. est sto h3abcont

. 
. *H4a&b - Hispanic population interaction
. logit benefit_immigonly i.treatgroup##c.ziphisppct2017s, vce(cluster statenum)
>  

Iteration 0:   log pseudolikelihood = -1119.2943  
Iteration 1:   log pseudolikelihood = -1107.9132  
Iteration 2:   log pseudolikelihood = -1107.8304  
Iteration 3:   log pseudolikelihood = -1107.8304  

Logistic regression                             Number of obs     =      2,126
                                                Wald chi2(5)      =      34.87
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1107.8304               Pseudo R2         =     0.0102

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1770583   .1927136    -0.92   0.358    -.5547701    .2006534
Fiscal thr..  |   .5325773   .1821141     2.92   0.003     .1756402    .8895144
              |
ziphisppct2~s |  -.0641492   .0851428    -0.75   0.451     -.231026    .1027277
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .2482045   .1445036     1.72   0.086    -.0350174    .5314264
Fiscal thr..  |   -.186278   .1199989    -1.55   0.121    -.4214716    .0489156
              |
        _cons |  -1.356701   .1103899   -12.29   0.000    -1.573061    -1.14034
-------------------------------------------------------------------------------

. est sto h4ab2017

. logit benefit_immigonly i.treatgroup##c.ziphisppct2017s immigscale $controls, 
> vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1068.3668  
Iteration 1:   log pseudolikelihood = -1002.3639  
Iteration 2:   log pseudolikelihood = -1000.5103  
Iteration 3:   log pseudolikelihood = -1000.5078  
Iteration 4:   log pseudolikelihood = -1000.5078  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(18)     =     218.90
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1000.5078               Pseudo R2         =     0.0635

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.2042638   .1764039    -1.16   0.247    -.5500091    .1414816
Fiscal thr..  |   .5834027   .1896128     3.08   0.002     .2117684     .955037
              |
ziphisppct2~s |  -.0573306   .0864923    -0.66   0.507    -.2268525    .1121913
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .2469323   .1715211     1.44   0.150     -.089243    .5831075
Fiscal thr..  |  -.1950297   .1219703    -1.60   0.110    -.4340872    .0440278
              |
   immigscale |   .2469352   .3090373     0.80   0.424    -.3587667    .8526371
        black |  -.0445659   .2530617    -0.18   0.860    -.5405578     .451426
     hispanic |   .0372638   .1537148     0.24   0.808    -.2640116    .3385392
    otherrace |  -.3791965   .2084674    -1.82   0.069    -.7877851    .0293922
         male |   .1067473   .1076223     0.99   0.321    -.1041885     .317683
       ideo01 |   .7058517    .305438     2.31   0.021     .1072043    1.304499
         pid7 |   .0501733    .039918     1.26   0.209    -.0280646    .1284112
       agecat |   .1217913   .0739343     1.65   0.099    -.0231173    .2666999
    incomecat |    .077815   .0229857     3.39   0.001     .0327638    .1228661
         educ |   .1637252   .0577355     2.84   0.005     .0505657    .2768847
    notbornus |   .2966949    .430744     0.69   0.491    -.5475478    1.140938
parents_not~s |   .2180917   .1916146     1.14   0.255     -.157466    .5936494
         sr2k |   .4291491    .316338     1.36   0.175     -.190862     1.04916
        _cons |  -3.614843   .2778725   -13.01   0.000    -4.159463   -3.070223
-------------------------------------------------------------------------------

. est sto h4abcont2017 

. logit benefit_immigonly i.treatgroup##c.ziphisppct2017s immigscale $controls i
> .statenum, vce(cluster statenum)

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 26.statenum != 0 predicts failure perfectly
      26.statenum dropped and 13 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 40.statenum != 0 predicts failure perfectly
      40.statenum dropped and 6 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1061.6154  
Iteration 1:   log pseudolikelihood = -971.62334  
Iteration 2:   log pseudolikelihood = -967.61623  
Iteration 3:   log pseudolikelihood = -967.58808  
Iteration 4:   log pseudolikelihood = -967.58807  

Logistic regression                             Number of obs     =      2,006
                                                Wald chi2(18)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -967.58807               Pseudo R2         =     0.0886

                               (Std. Err. adjusted for 47 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.2002977   .1984158    -1.01   0.313    -.5891856    .1885902
Fiscal thr..  |   .6302083   .2082262     3.03   0.002     .2220924    1.038324
              |
ziphisppct2~s |  -.1023701   .0738211    -1.39   0.166    -.2470569    .0423166
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .2364015   .1738862     1.36   0.174    -.1044092    .5772121
Fiscal thr..  |   -.210381   .1299873    -1.62   0.106    -.4651514    .0443895
              |
   immigscale |   .3547975   .3208048     1.11   0.269    -.2739684    .9835634
        black |  -.1565823   .2692877    -0.58   0.561    -.6843766    .3712119
     hispanic |   .0151837    .163397     0.09   0.926    -.3050686     .335436
    otherrace |  -.4317894     .21261    -2.03   0.042    -.8484974   -.0150815
         male |   .0966086   .1169898     0.83   0.409    -.1326872    .3259044
       ideo01 |   .7078252   .3044035     2.33   0.020     .1112053    1.304445
         pid7 |   .0492484   .0398782     1.23   0.217    -.0289114    .1274081
       agecat |   .0944447   .0748812     1.26   0.207    -.0523198    .2412092
    incomecat |   .0779227   .0248905     3.13   0.002     .0291381    .1267072
         educ |   .1689408   .0589798     2.86   0.004     .0533425     .284539
    notbornus |   .3465415    .466215     0.74   0.457    -.5672231    1.260306
parents_not~s |   .1743196   .1942321     0.90   0.369    -.2063683    .5550076
         sr2k |   .3823935   .3373016     1.13   0.257    -.2787055    1.043493
              |
     statenum |
          AK  |          0  (empty)
          AL  |  -1.963647   .1299733   -15.11   0.000     -2.21839   -1.708904
          AR  |  -1.162981   .1114278   -10.44   0.000    -1.381376   -.9445868
          AZ  |  -1.029318   .1362341    -7.56   0.000    -1.296332    -.762304
          CA  |   -1.07304   .1503969    -7.13   0.000    -1.367812   -.7782673
          CO  |  -1.074162   .1110754    -9.67   0.000    -1.291866   -.8564583
          CT  |  -.9512585   .1449753    -6.56   0.000    -1.235405   -.6671121
          DC  |  -.5803505   .1613637    -3.60   0.000    -.8966176   -.2640834
          DE  |  -.6817533   .1208609    -5.64   0.000    -.9186363   -.4448702
          FL  |  -1.322272   .1165171   -11.35   0.000    -1.550641   -1.093902
          GA  |  -.6529748   .1399035    -4.67   0.000    -.9271806    -.378769
          HI  |    .085375   .2351926     0.36   0.717    -.3755941    .5463441
          IA  |   -.829841   .1439251    -5.77   0.000    -1.111929   -.5477531
          ID  |  -1.455416   .1219405   -11.94   0.000    -1.694415   -1.216417
          IL  |  -1.687462   .1187026   -14.22   0.000    -1.920115    -1.45481
          IN  |  -1.979979   .1234054   -16.04   0.000    -2.221849   -1.738109
          KS  |  -1.883338   .1428364   -13.19   0.000    -2.163292   -1.603384
          KY  |  -2.031433   .1319284   -15.40   0.000    -2.290008   -1.772858
          LA  |  -1.501273   .1531161    -9.80   0.000    -1.801375   -1.201171
          MA  |  -2.012204   .1527277   -13.18   0.000    -2.311544   -1.712863
          MD  |  -1.464681   .1400862   -10.46   0.000    -1.739245   -1.190117
          ME  |  -.6598998   .1330632    -4.96   0.000    -.9206989   -.3991007
          MI  |  -1.747043   .1255911   -13.91   0.000    -1.993197   -1.500889
          MN  |  -1.020084   .1347164    -7.57   0.000    -1.284123   -.7560443
          MO  |  -1.100271   .1109517    -9.92   0.000    -1.317733   -.8828099
          MS  |          0  (empty)
          MT  |  -1.512343   .1945697    -7.77   0.000    -1.893692   -1.130993
          NC  |  -1.436924   .1101947   -13.04   0.000    -1.652901   -1.220946
          ND  |          0  (empty)
          NE  |  -.6663634    .165541    -4.03   0.000    -.9908178   -.3419089
          NH  |  -.0611008   .1489805    -0.41   0.682    -.3530972    .2308956
          NJ  |  -1.163463   .1448824    -8.03   0.000    -1.447427   -.8794989
          NM  |  -1.771858   .1447396   -12.24   0.000    -2.055542   -1.488174
          NV  |  -2.206868   .1232211   -17.91   0.000    -2.448377   -1.965359
          NY  |  -1.572428   .1429172   -11.00   0.000    -1.852541   -1.292316
          OH  |  -2.038894   .1073493   -18.99   0.000    -2.249295   -1.828494
          OK  |  -2.613374   .1476246   -17.70   0.000    -2.902713   -2.324035
          OR  |  -1.773079   .1502339   -11.80   0.000    -2.067532   -1.478626
          PA  |  -1.467913   .1213967   -12.09   0.000    -1.705846    -1.22998
          RI  |          0  (empty)
          SC  |  -1.703056   .1077412   -15.81   0.000    -1.914224   -1.491887
          SD  |  -1.784233   .1301528   -13.71   0.000    -2.039328   -1.529138
          TN  |  -1.169036   .1003745   -11.65   0.000    -1.365766   -.9723054
          TX  |  -1.416572   .1259418   -11.25   0.000    -1.663413   -1.169731
          UT  |  -3.022075   .1224805   -24.67   0.000    -3.262132   -2.782017
          VA  |  -1.196882    .129554    -9.24   0.000    -1.450803   -.9429607
          VT  |  -1.652045   .1595539   -10.35   0.000    -1.964764   -1.339325
          WA  |   -1.56284   .1314749   -11.89   0.000    -1.820526   -1.305154
          WI  |  -1.428449   .1142835   -12.50   0.000    -1.652441   -1.204458
          WV  |  -1.939426   .1326269   -14.62   0.000     -2.19937   -1.679482
          WY  |          0  (omitted)
              |
        _cons |  -2.123087   .3631854    -5.85   0.000    -2.834917   -1.411257
-------------------------------------------------------------------------------

. est sto h4abcontfe2017

. 
. *Figure 4 - heterogeneous effects by local Hispanic population (non-standardiz
> ed population variable used for display)
. qui logit benefit_immigonly i.treatgroup##c.ziphisppct2017 immigscale $control
> s, vce(cluster statenum) 

. margins, over(treatgroup) at(ziphisppct2017=(0(10)60))

Predictive margins                              Number of obs     =      2,028
Model VCE    : Robust

Expression   : Pr(benefit_immigonly), predict()
over         : treatgroup

1._at        : 1.treatgroup
                   ziphisppct~7    =           0
               2.treatgroup
                   ziphisppct~7    =           0
               3.treatgroup
                   ziphisppct~7    =           0

2._at        : 1.treatgroup
                   ziphisppct~7    =          10
               2.treatgroup
                   ziphisppct~7    =          10
               3.treatgroup
                   ziphisppct~7    =          10

3._at        : 1.treatgroup
                   ziphisppct~7    =          20
               2.treatgroup
                   ziphisppct~7    =          20
               3.treatgroup
                   ziphisppct~7    =          20

4._at        : 1.treatgroup
                   ziphisppct~7    =          30
               2.treatgroup
                   ziphisppct~7    =          30
               3.treatgroup
                   ziphisppct~7    =          30

5._at        : 1.treatgroup
                   ziphisppct~7    =          40
               2.treatgroup
                   ziphisppct~7    =          40
               3.treatgroup
                   ziphisppct~7    =          40

6._at        : 1.treatgroup
                   ziphisppct~7    =          50
               2.treatgroup
                   ziphisppct~7    =          50
               3.treatgroup
                   ziphisppct~7    =          50

7._at        : 1.treatgroup
                   ziphisppct~7    =          60
               2.treatgroup
                   ziphisppct~7    =          60
               3.treatgroup
                   ziphisppct~7    =          60

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
          _at#|
   treatgroup |
   1#Control  |   .2013008    .016693    12.06   0.000     .1685831    .2340184
           1 #|
Cultural t..  |   .1772174   .0175148    10.12   0.000      .142889    .2115457
           1 #|
Fiscal thr..  |   .3081826   .0300753    10.25   0.000     .2492362    .3671291
   2#Control  |   .1968371   .0130235    15.11   0.000     .1713116    .2223627
           2 #|
Cultural t..  |    .191204   .0143483    13.33   0.000     .1630819    .2193262
           2 #|
Fiscal thr..  |   .2827047    .023936    11.81   0.000      .235791    .3296184
   3#Control  |   .1924452   .0124006    15.52   0.000     .1681405      .21675
           3 #|
Cultural t..  |   .2059772   .0147382    13.98   0.000     .1770908    .2348636
           3 #|
Fiscal thr..  |   .2584811   .0205208    12.60   0.000     .2182611    .2987011
   4#Control  |    .188125   .0149164    12.61   0.000     .1588893    .2173607
           4 #|
Cultural t..  |   .2215364    .019574    11.32   0.000     .1831721    .2599008
           4 #|
Fiscal thr..  |   .2355852   .0201685    11.68   0.000     .1960556    .2751148
   5#Control  |   .1838764   .0191688     9.59   0.000     .1463063    .2214465
           5 #|
Cultural t..  |    .237875   .0273833     8.69   0.000     .1842048    .2915452
           5 #|
Fiscal thr..  |   .2140663    .022089     9.69   0.000     .1707727      .25736
   6#Control  |   .1796992   .0240951     7.46   0.000     .1324736    .2269248
           6 #|
Cultural t..  |   .2549792   .0369559     6.90   0.000      .182547    .3274114
           6 #|
Fiscal thr..  |    .193951   .0250225     7.75   0.000     .1449078    .2429941
   7#Control  |   .1755933   .0292268     6.01   0.000     .1183098    .2328768
           7 #|
Cultural t..  |   .2728284   .0477429     5.71   0.000     .1792541    .3664027
           7 #|
Fiscal thr..  |   .1752444    .028085     6.24   0.000     .1201988      .23029
-------------------------------------------------------------------------------

. marginsplot, ytitle("Probability of assuming only immigrants benefit") title("
> ") ylabel(0(.1).4) legend(ring(0) pos(12) col(1)) addplot(histogram ziphisppct
> 2017 if ziphisppct2017<=60, legend(order(4 "Control" 5 "Cultural threat prime"
>  6 "Fiscal threat prime")) below) xtitle("% Hispanic population in ZIP code") 
> scheme(s1mono) level(90) recast(line) recastci(rarea) ci1opts(fcolor(%30)) ci2
> opts(fcolor(%30)) ci3opts(fcolor(%30))

  Variables that uniquely identify margins: ziphisppct2017 treatgroup

. graph export Figure4.tif, as(tif) replace
(file Figure4.tif written in TIFF format)

. 
. *Table 1
. esttab h12 h3ab h3abcont h4ab2017 h4abcont2017 h4abcontfe2017 using Table1, re
> place tab label wrap varwidth(25) b(2) se(2) star(* 0.1 ** 0.05 *** .01) scala
> rs("N Observations" "r2_p Pseudo R-squared") title(Table 1. Logit Models of th
> e Assumption that the Described Policy will Benefit Immigrants and Not Native-
> Born Americans, with State-Clustered Standard Errors.) nobaselevels eqlabels(n
> one) interaction(" X ") substitute(=1 "" \% % ) order(2.treatgroup 3.treatgrou
> p immigscale 2.treatgroup#c.immigscale 3.treatgroup#c.immigscale ziphisppct201
> 7s 2.treatgroup#c.ziphisppct2017s 3.treatgroup#c.ziphisppct2017s) indicate("Co
> ntrols = $controls" "State fixed effects=*statenum") nomtitles nolz addnote(Hi
> spanic population percentage measured in standard deviations)
(output written to Table1.txt)

. 
. //POLICY APPROVAL
. reg approval i.benefit_immigonly##c.immigscale i.treatgroup, robust/*H5 suppor
> ted*/

Linear regression                               Number of obs     =      2,126
                                                F(5, 2120)        =      68.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1260
                                                Root MSE          =     1.8045

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_i~y |   .3201006   .2037704     1.57   0.116    -.0795102    .7197114
   immigscale |  -.6752153   .2069189    -3.26   0.001    -1.081001   -.2694301
              |
benefit_imm~y#|
 c.immigscale |
           1  |  -2.901958   .3654868    -7.94   0.000    -3.618708   -2.185208
              |
   treatgroup |
Cultural t..  |   .0641662   .0951818     0.67   0.500    -.1224932    .2508257
Fiscal thr..  |    .101832   .0961946     1.06   0.290    -.0868136    .2904777
              |
        _cons |   5.154995   .1130332    45.61   0.000     4.933327    5.376662
-------------------------------------------------------------------------------

. est sto approval_nocont

. reg approval i.benefit_immigonly##c.immigscale i.treatgroup ideo01 sr2k, robus
> t

Linear regression                               Number of obs     =      2,106
                                                F(7, 2098)        =      90.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2021
                                                Root MSE          =     1.7283

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_i~y |   .2474967   .2031209     1.22   0.223    -.1508428    .6458363
   immigscale |   .7044723     .22515     3.13   0.002     .2629317    1.146013
              |
benefit_imm~y#|
 c.immigscale |
           1  |  -2.465268   .3632123    -6.79   0.000    -3.177562   -1.752974
              |
   treatgroup |
Cultural t..  |   .0484172   .0913002     0.53   0.596    -.1306313    .2274656
Fiscal thr..  |   .0690711   .0929177     0.74   0.457    -.1131494    .2512917
              |
       ideo01 |  -1.535959   .1650669    -9.31   0.000    -1.859671   -1.212247
         sr2k |  -1.298461   .1905781    -6.81   0.000    -1.672203   -.9247192
        _cons |   5.950348   .1223269    48.64   0.000     5.710453    6.190242
-------------------------------------------------------------------------------

. est sto approval_ideosr

. reg approval i.benefit_immigonly##c.immigscale i.treatgroup $controls, robust 

Linear regression                               Number of obs     =      2,106
                                                F(17, 2088)       =      55.75
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2731
                                                Root MSE          =     1.6535

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_i~y |   .3328412   .1956909     1.70   0.089    -.0509283    .7166107
   immigscale |    .639368   .2239334     2.86   0.004     .2002119    1.078524
              |
benefit_imm~y#|
 c.immigscale |
           1  |  -2.240671   .3453863    -6.49   0.000    -2.918009   -1.563334
              |
   treatgroup |
Cultural t..  |   .0794157   .0875192     0.91   0.364    -.0922181    .2510496
Fiscal thr..  |   .0637858   .0892191     0.71   0.475    -.1111819    .2387536
              |
        black |  -.2233536   .1230467    -1.82   0.070    -.4646606    .0179534
     hispanic |  -.1278793    .158605    -0.81   0.420    -.4389197    .1831611
    otherrace |    .090603   .1574759     0.58   0.565    -.2182232    .3994291
         male |   .0203983   .0737968     0.28   0.782    -.1243247    .1651213
       ideo01 |  -1.223187   .1777779    -6.88   0.000    -1.571827   -.8745467
         pid7 |  -.1084396   .0233125    -4.65   0.000    -.1541578   -.0627215
       agecat |  -.1367836   .0414632    -3.30   0.001    -.2180971   -.0554701
    incomecat |   -.116074   .0134704    -8.62   0.000    -.1424907   -.0896573
         educ |  -.1249102   .0361508    -3.46   0.001    -.1958055   -.0540149
    notbornus |   .4041674    .209302     1.93   0.054    -.0062949    .8146298
parents_not~s |  -.1988801   .1161625    -1.71   0.087    -.4266865    .0289263
         sr2k |  -1.148917   .1924864    -5.97   0.000    -1.526402   -.7714317
        _cons |   7.440265   .1868989    39.81   0.000     7.073737    7.806792
-------------------------------------------------------------------------------

. est sto approval_cont

. 
. *Figure 5 - assumption/attitude interaction
. margins, over(benefit_immigonly) at(immigscale=(0(.2)1)) 

Predictive margins                              Number of obs     =      2,106
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : benefit_immigonly

1._at        : 0.benefit_immigonly
                   immigscale      =           0
               1.benefit_immigonly
                   immigscale      =           0

2._at        : 0.benefit_immigonly
                   immigscale      =          .2
               1.benefit_immigonly
                   immigscale      =          .2

3._at        : 0.benefit_immigonly
                   immigscale      =          .4
               1.benefit_immigonly
                   immigscale      =          .4

4._at        : 0.benefit_immigonly
                   immigscale      =          .6
               1.benefit_immigonly
                   immigscale      =          .6

5._at        : 0.benefit_immigonly
                   immigscale      =          .8
               1.benefit_immigonly
                   immigscale      =          .8

6._at        : 0.benefit_immigonly
                   immigscale      =           1
               1.benefit_immigonly
                   immigscale      =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
          _at#|
benefit_imm~y |
         1 0  |    4.55639   .1150486    39.60   0.000     4.330768    4.782012
         1 1  |   4.455684   .1872341    23.80   0.000     4.088499    4.822869
         2 0  |   4.684264    .074922    62.52   0.000     4.537334    4.831194
         2 1  |   4.135423   .1308655    31.60   0.000     3.878783    4.392063
         3 0  |   4.812138   .0447453   107.55   0.000     4.724388    4.899887
         3 1  |   3.815162   .0860998    44.31   0.000     3.646312    3.984013
         4 0  |   4.940011   .0490169   100.78   0.000     4.843884    5.036138
         4 1  |   3.494902    .076934    45.43   0.000     3.344026    3.645777
         5 0  |   5.067885   .0825523    61.39   0.000     4.905991    5.229778
         5 1  |   3.174641     .11244    28.23   0.000     2.954135    3.395147
         6 0  |   5.195758   .1234455    42.09   0.000     4.953669    5.437847
         6 1  |    2.85438   .1660871    17.19   0.000     2.528667    3.180094
-------------------------------------------------------------------------------

. marginsplot, ytitle(Predicted policy approval) title("") legend(ring(0) pos(10
> ) col(1)) recast(line) recastci(rarea) ci1opts(fcolor(%30)) ci2opts(fcolor(%30
> )) addplot(histogram immigscale, ylabel(0(1)7) legend(order(4 "Immigrant-only 
> assumption" 3 "No immigrant-only assumption")) below) scheme(s1mono) level(90)

  Variables that uniquely identify margins: immigscale benefit_immigonly

. graph export Figure5.tif, as(tif) replace 
(file Figure5.tif written in TIFF format)

. 
. reg approval i.benefit_bornus##c.immigscale i.benefit_immig i.benefit_immig#c.
> immigscale i.treatgroup, robust 

Linear regression                               Number of obs     =      2,126
                                                F(7, 2118)        =      56.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1416
                                                Root MSE          =     1.7891

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_b~s |   .1889554   .1879662     1.01   0.315    -.1796623    .5575731
   immigscale |   -.781312   .2556322    -3.06   0.002    -1.282628   -.2799956
              |
benefit_bor~s#|
 c.immigscale |
           1  |   1.488312   .3675715     4.05   0.000     .7674736    2.209151
              |
1.benefit_i~g |   .6700133   .1800984     3.72   0.000     .3168251    1.023202
              |
benefit_immig#|
 c.immigscale |
           1  |  -2.784714   .3395574    -8.20   0.000    -3.450615   -2.118813
              |
   treatgroup |
Cultural t..  |   .1093242   .0941717     1.16   0.246    -.0753545     .294003
Fiscal thr..  |   .1001988   .0958851     1.04   0.296      -.08784    .2882376
              |
        _cons |   4.931872    .136035    36.25   0.000     4.665096    5.198648
-------------------------------------------------------------------------------

. est sto approval_sepintnc

. reg approval i.benefit_bornus##c.immigscale i.benefit_immig i.benefit_immig#c.
> immigscale i.treatgroup $controls, robust 

Linear regression                               Number of obs     =      2,106
                                                F(19, 2086)       =      50.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2827
                                                Root MSE          =     1.6433

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_b~s |   .0254444   .1835949     0.14   0.890    -.3346039    .3854927
   immigscale |   .5109911   .2566631     1.99   0.047     .0076487    1.014333
              |
benefit_bor~s#|
 c.immigscale |
           1  |   1.258747   .3500632     3.60   0.000     .5722373    1.945257
              |
1.benefit_i~g |   .5239399   .1752762     2.99   0.003     .1802055    .8676743
              |
benefit_immig#|
 c.immigscale |
           1  |  -2.124327    .322634    -6.58   0.000    -2.757045   -1.491609
              |
   treatgroup |
Cultural t..  |   .1104024   .0869097     1.27   0.204    -.0600364    .2808412
Fiscal thr..  |   .0689818   .0888121     0.78   0.437    -.1051879    .2431514
              |
        black |  -.2403147   .1241406    -1.94   0.053    -.4837671    .0031377
     hispanic |   -.128344   .1581689    -0.81   0.417    -.4385292    .1818412
    otherrace |   .0965059   .1579144     0.61   0.541    -.2131803    .4061921
         male |    .041727   .0732057     0.57   0.569    -.1018369    .1852909
       ideo01 |  -1.187372     .17786    -6.68   0.000    -1.536173   -.8385704
         pid7 |  -.1070242   .0232308    -4.61   0.000    -.1525822   -.0614662
       agecat |  -.1493388   .0413722    -3.61   0.000    -.2304739   -.0682038
    incomecat |   -.112492   .0134653    -8.35   0.000    -.1388987   -.0860853
         educ |  -.1222197   .0356651    -3.43   0.001    -.1921626   -.0522768
    notbornus |   .4568103   .2067861     2.21   0.027     .0512818    .8623388
parents_not~s |  -.1966089    .114875    -1.71   0.087    -.4218905    .0286727
         sr2k |  -1.143121   .1920685    -5.95   0.000    -1.519787   -.7664552
        _cons |    7.28984   .1997578    36.49   0.000     6.898094    7.681585
-------------------------------------------------------------------------------

. est sto approval_sepint

. 
. esttab approval_nocont approval_ideosr approval_cont approval_sepintnc approva
> l_sepint using Table2, replace tab label wrap varwidth(25) b(%12.2g) se(%12.2g
> ) star(* 0.1 ** 0.05 *** .01) scalars("N Observations" "r2 R-squared") title(T
> able 2. Linear Regression Models of Policy Approval, with Interaction Terms an
> d Robust Standard Errors.) nobaselevels eqlabels(none) interaction(" X ") subs
> titute(=1 "" "Anti-immigration scale" "Anti-immigration") order(1.benefit_immi
> gonly immigscale 1.benefit_immigonly#c.immigscale 1.benefit_immig 1.benefit_im
> mig#c.immigscale 1.benefit_bornus 1.benefit_bornus#c.immigscale 2.treatgroup 3
> .treatgroup ideo01 sr2k) indicate(Controls = black hispanic otherrace male pid
> 7 agecat incomecat educ notbornus parents_notbornus) nomtitles nolz
(output written to Table2.txt)

. 
. //APPENDICES
. 
. //Appendix D - Full Results with Control Variables
. *Table D.1
. esttab h3abcont h4abcont2017 h4abcontfe2017 using TableD1, replace tab label w
> rap varwidth(25) cells("b(star fmt(2)) se(fmt(2) par)") onecell star(* .1 ** .
> 05 *** .01) scalars("N Observations" "r2_p Pseudo R-squared") title(Table D.1.
>  Logit Models of the Assumption that the Described Policy will Benefit Immigra
> nts and Not Native-Born Americans, with Interaction Terms and all Controls Dis
> played (Table 1 in main text)) nobaselevels eqlabels(none) interaction(" X ") 
> substitute(=1 "") order(2.treatgroup 3.treatgroup immigscale 2.treatgroup#c.im
> migscale 3.treatgroup#c.immigscale ziphisppct2017s 2.treatgroup#c.ziphisppct20
> 17s 3.treatgroup#c.ziphisppct2017s) indicate("State fixed effects=*statenum" )
>  nomtitles collabels(none) addnotes("State-clustered standard errors in parent
> heses" "Hispanic population percentage measured in standard deviations" "* p<0
> .1, ** p<0.05, *** p<.01") nolz
(output written to TableD1.txt)

. 
. *Table D.2
. esttab approval_cont approval_sepint using TableD2, replace tab label wrap var
> width(25) cells("b(star fmt(2)) se(fmt(2) par)") onecell star(* 0.1 ** 0.05 **
> * .01) scalars("N Observations" "r2 R-squared") title(Table D.2. Linear Regres
> sion Models of Policy Approval, with Interaction Terms and Robust Standard Err
> ors (Table 2 in main text)) nobaselevels eqlabels(none) interaction(" X ") sub
> stitute(=1 "") order(1.benefit_immigonly immigscale 1.benefit_immigonly#c.immi
> gscale 1.benefit_immig 1.benefit_immig#c.immigscale 1.benefit_bornus 1.benefit
> _bornus#c.immigscale 2.treatgroup 3.treatgroup ideo01 sr2k) nomtitles collabel
> s(none) addnotes("Robust standard errors in parentheses" "* p<0.1, ** p<0.05, 
> *** p<.01") nolz
(output written to TableD2.txt)

. 
. //Appendix E - Alternative Specifications
. *Table E.1 - Immigrants
. logit benefit_immig i.treatgroup, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1446.7942  
Iteration 1:   log pseudolikelihood = -1441.4271  
Iteration 2:   log pseudolikelihood = -1441.4244  
Iteration 3:   log pseudolikelihood = -1441.4244  

Logistic regression                             Number of obs     =      2,208
                                                Wald chi2(2)      =      11.72
                                                Prob > chi2       =     0.0029
Log pseudolikelihood = -1441.4244               Pseudo R2         =     0.0037

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_immig |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.0080661   .1074143    -0.08   0.940    -.2185943    .2024621
Fiscal thr..  |   .3022067   .1017216     2.97   0.003      .102836    .5015774
              |
        _cons |  -.6608863   .0668569    -9.89   0.000    -.7919234   -.5298492
-------------------------------------------------------------------------------

. est sto immig_h12

. logit benefit_immig i.treatgroup##c.immigscale ziphisppct2017s $controls, vce(
> cluster statenum) 

Iteration 0:   log pseudolikelihood = -1335.6363  
Iteration 1:   log pseudolikelihood = -1301.9811  
Iteration 2:   log pseudolikelihood = -1301.8847  
Iteration 3:   log pseudolikelihood = -1301.8847  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(18)     =     108.23
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1301.8847               Pseudo R2         =     0.0253

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_immig |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1673475   .2108086    -0.79   0.427    -.5805248    .2458297
Fiscal thr..  |   .0954296   .2633204     0.36   0.717    -.4206689    .6115281
              |
   immigscale |  -.4591527   .4492317    -1.02   0.307    -1.339631    .4213253
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |   .2307087   .4845152     0.48   0.634    -.7189235    1.180341
Fiscal thr..  |   .4379769   .5650996     0.78   0.438     -.669598    1.545552
              |
ziphisppct2~s |  -.1120871   .0391057    -2.87   0.004    -.1887328   -.0354414
        black |   -.116326    .182557    -0.64   0.524    -.4741311    .2414791
     hispanic |   .1649596   .1998101     0.83   0.409     -.226661    .5565801
    otherrace |  -.2210883     .24802    -0.89   0.373    -.7071987     .265022
         male |    .110536   .0958973     1.15   0.249    -.0774193    .2984912
       ideo01 |   .1909738    .295947     0.65   0.519    -.3890716    .7710192
         pid7 |   .0483024   .0268028     1.80   0.072    -.0042301    .1008348
       agecat |   .1158823   .0596277     1.94   0.052    -.0009859    .2327506
    incomecat |   .0292456   .0159023     1.84   0.066    -.0019224    .0604136
         educ |    .126231   .0412396     3.06   0.002     .0454029    .2070592
    notbornus |  -.0592023   .3309655    -0.18   0.858    -.7078828    .5894783
parents_not~s |   .3799083   .1522573     2.50   0.013     .0814894    .6783271
         sr2k |  -.5151137   .2310228    -2.23   0.026    -.9679101   -.0623172
        _cons |   -1.19215   .2304523    -5.17   0.000    -1.643828   -.7404722
-------------------------------------------------------------------------------

. est sto immig_h3ab

. logit benefit_immig i.treatgroup##c.ziphisppct2017s immigscale $controls i.sta
> tenum, vce(cluster statenum)

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1333.7183  
Iteration 1:   log pseudolikelihood = -1268.9674  
Iteration 2:   log pseudolikelihood = -1268.0913  
Iteration 3:   log pseudolikelihood = -1268.0831  
Iteration 4:   log pseudolikelihood = -1268.0831  

Logistic regression                             Number of obs     =      2,025
                                                Wald chi2(18)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -1268.0831               Pseudo R2         =     0.0492

                               (Std. Err. adjusted for 49 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_immig |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1051936   .1481856    -0.71   0.478    -.3956321    .1852449
Fiscal thr..  |   .4773251   .1534198     3.11   0.002     .1766279    .7780223
              |
ziphisppct2~s |  -.0669656   .0719923    -0.93   0.352     -.208068    .0741367
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .0567973   .1083194     0.52   0.600    -.1555048    .2690995
Fiscal thr..  |  -.1690528   .0929805    -1.82   0.069    -.3512912    .0131856
              |
   immigscale |  -.1857666   .2931089    -0.63   0.526    -.7602494    .3887162
        black |  -.1875161   .1900215    -0.99   0.324    -.5599513    .1849192
     hispanic |   .2107739   .2011251     1.05   0.295     -.183424    .6049718
    otherrace |  -.2101448   .2469954    -0.85   0.395    -.6942468    .2739572
         male |   .1162194   .1023184     1.14   0.256     -.084321    .3167599
       ideo01 |   .1902967    .303518     0.63   0.531    -.4045877    .7851811
         pid7 |   .0454251   .0274053     1.66   0.097    -.0082884    .0991385
       agecat |    .095253   .0622603     1.53   0.126    -.0267749    .2172808
    incomecat |   .0292268   .0171415     1.71   0.088      -.00437    .0628236
         educ |   .1306755   .0432902     3.02   0.003     .0458284    .2155227
    notbornus |  -.0165506   .3412719    -0.05   0.961    -.6854312      .65233
parents_not~s |   .3308512   .1628343     2.03   0.042     .0117017    .6500007
         sr2k |  -.5095268    .240343    -2.12   0.034    -.9805904   -.0384632
              |
     statenum |
          AK  |          0  (empty)
          AL  |  -1.713295   .1064117   -16.10   0.000    -1.921858   -1.504732
          AR  |  -.8252888    .095559    -8.64   0.000    -1.012581   -.6379967
          AZ  |  -.9772335    .130033    -7.52   0.000    -1.232093   -.7223736
          CA  |  -1.075327   .1224601    -8.78   0.000    -1.315345   -.8353099
          CO  |  -.5509847   .1021127    -5.40   0.000    -.7511219   -.3508475
          CT  |  -1.332051   .1303479   -10.22   0.000    -1.587528   -1.076574
          DC  |  -.2787604   .1277799    -2.18   0.029    -.5292044   -.0283164
          DE  |  -1.185619   .1076656   -11.01   0.000     -1.39664   -.9745981
          FL  |  -1.207214   .1077353   -11.21   0.000    -1.418372   -.9960571
          GA  |  -.6136331   .1306963    -4.70   0.000    -.8697932    -.357473
          HI  |   -.619841   .2133303    -2.91   0.004    -1.037961   -.2017212
          IA  |  -.6190065   .1239311    -4.99   0.000    -.8619071    -.376106
          ID  |  -1.728835   .1079971   -16.01   0.000    -1.940505   -1.517164
          IL  |  -1.491489   .1147756   -12.99   0.000    -1.716445   -1.266533
          IN  |  -1.685765   .0957301   -17.61   0.000    -1.873393   -1.498138
          KS  |  -1.298733   .1231415   -10.55   0.000    -1.540086    -1.05738
          KY  |  -1.397763   .1171633   -11.93   0.000    -1.627399   -1.168127
          LA  |  -1.333907   .1198708   -11.13   0.000     -1.56885   -1.098965
          MA  |  -2.390279    .133364   -17.92   0.000    -2.651667    -2.12889
          MD  |   -1.30971    .136156    -9.62   0.000    -1.576571   -1.042849
          ME  |  -.6090363   .1155875    -5.27   0.000    -.8355836    -.382489
          MI  |  -1.214365   .1079229   -11.25   0.000     -1.42589    -1.00284
          MN  |  -.7366787   .1197082    -6.15   0.000    -.9713025    -.502055
          MO  |  -.8676381   .0873631    -9.93   0.000    -1.038867   -.6964095
          MS  |  -2.936526   .0975069   -30.12   0.000    -3.127636   -2.745416
          MT  |  -2.039895   .1082079   -18.85   0.000    -2.251978   -1.827811
          NC  |  -1.003081   .0982976   -10.20   0.000    -1.195741    -.810421
          ND  |          0  (empty)
          NE  |   .4710735   .1321881     3.56   0.000     .2119896    .7301574
          NH  |  -.6155216   .1148877    -5.36   0.000    -.8406974   -.3903458
          NJ  |  -1.098766   .1396831    -7.87   0.000     -1.37254   -.8249927
          NM  |  -.9387831   .1443185    -6.50   0.000    -1.221642    -.655924
          NV  |  -1.665688   .1210076   -13.77   0.000    -1.902859   -1.428518
          NY  |  -1.295233   .1210999   -10.70   0.000    -1.532585   -1.057882
          OH  |  -1.399571   .0962607   -14.54   0.000    -1.588238   -1.210903
          OK  |   -1.84938   .1135115   -16.29   0.000    -2.071859   -1.626902
          OR  |  -1.067373   .1032374   -10.34   0.000    -1.269715   -.8650312
          PA  |  -1.101611    .109758   -10.04   0.000    -1.316733   -.8864893
          RI  |  -2.322037   .1580535   -14.69   0.000    -2.631816   -2.012258
          SC  |   -1.49262   .1028158   -14.52   0.000    -1.694136   -1.291105
          SD  |  -1.874505   .1110547   -16.88   0.000    -2.092169   -1.656842
          TN  |  -1.063487   .0922674   -11.53   0.000    -1.244328   -.8826459
          TX  |  -1.332835   .1133675   -11.76   0.000    -1.555031   -1.110638
          UT  |  -1.204962   .1119998   -10.76   0.000    -1.424477   -.9854463
          VA  |  -.6363112   .1184266    -5.37   0.000    -.8684231   -.4041993
          VT  |  -.6469144   .1358347    -4.76   0.000    -.9131456   -.3806832
          WA  |  -.7815168   .1134359    -6.89   0.000    -1.003847   -.5591865
          WI  |  -1.254212   .1037339   -12.09   0.000    -1.457527   -1.050897
          WV  |  -1.468011   .1086613   -13.51   0.000    -1.680983   -1.255039
          WY  |          0  (omitted)
              |
        _cons |  -.1273817   .2796725    -0.46   0.649    -.6755297    .4207664
-------------------------------------------------------------------------------

. est sto immig_h4ab

. esttab immig_h12 immig_h3ab immig_h4ab using TableE1, replace tab label wrap v
> arwidth(25) cells("b(star fmt(2)) se(fmt(2) par)") onecell star(* .1 ** .05 **
> * .01) scalars("N Observations" "r2_p Pseudo R-squared") title(Table E.1. Logi
> t Models of the Assumption that the Described Policy will Benefit Immigrants, 
> with Interaction Terms) nobaselevels eqlabels(none) interaction(" X ") substit
> ute(=1 "") order(2.treatgroup 3.treatgroup immigscale 2.treatgroup#c.immigscal
> e 3.treatgroup#c.immigscale ziphisppct2017s 2.treatgroup#c.ziphisppct2017s 3.t
> reatgroup#c.ziphisppct2017s) indicate("State fixed effects=*statenum" ) nomtit
> les collabels(none) addnotes("State-clustered standard errors in parentheses" 
> "Hispanic population percentage measured in standard deviations" "* p<0.1, ** 
> p<0.05, *** p<.01") nolz 
(output written to TableE1.txt)

. 
. *Table E.2 - Born in US
. logit benefit_bornus i.treatgroup, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1296.5385  
Iteration 1:   log pseudolikelihood = -1293.7608  
Iteration 2:   log pseudolikelihood = -1293.7581  
Iteration 3:   log pseudolikelihood = -1293.7581  

Logistic regression                             Number of obs     =      2,208
                                                Wald chi2(2)      =       4.21
                                                Prob > chi2       =     0.1216
Log pseudolikelihood = -1293.7581               Pseudo R2         =     0.0021

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_bor~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.2748449   .1371917    -2.00   0.045    -.5437357   -.0059541
Fiscal thr..  |  -.1146984    .110965    -1.03   0.301    -.3321858    .1027889
              |
        _cons |  -.8472979   .0919746    -9.21   0.000    -1.027565    -.667031
-------------------------------------------------------------------------------

. est sto bornus_h12

. logit benefit_bornus i.treatgroup##c.immigscale ziphisppct2017s $controls, vce
> (cluster statenum) 

Iteration 0:   log pseudolikelihood = -1190.1799  
Iteration 1:   log pseudolikelihood = -1147.1565  
Iteration 2:   log pseudolikelihood = -1146.4806  
Iteration 3:   log pseudolikelihood = -1146.4797  
Iteration 4:   log pseudolikelihood = -1146.4797  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(18)     =     253.93
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1146.4797               Pseudo R2         =     0.0367

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_bor~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.3680388    .294937    -1.25   0.212    -.9461047     .210027
Fiscal thr..  |  -.1512489   .2866111    -0.53   0.598    -.7129963    .4104986
              |
   immigscale |  -.0367346   .4530486    -0.08   0.935    -.9246936    .8512245
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |   .0972627   .5910057     0.16   0.869    -1.061087    1.255613
Fiscal thr..  |  -.0296221    .508241    -0.06   0.954    -1.025756    .9665118
              |
ziphisppct2~s |  -.0608889   .0567809    -1.07   0.284    -.1721775    .0503997
        black |  -.0291074   .1361241    -0.21   0.831    -.2959057    .2376909
     hispanic |   .0728626   .1762887     0.41   0.679    -.2726568     .418382
    otherrace |   .2381737   .1789259     1.33   0.183    -.1125146    .5888619
         male |  -.1772433   .0896028    -1.98   0.048    -.3528615   -.0016251
       ideo01 |  -.5414088   .2509654    -2.16   0.031    -1.033292   -.0495257
         pid7 |  -.0438955   .0310199    -1.42   0.157    -.1046933    .0169023
       agecat |   .0284546   .0543985     0.52   0.601    -.0781645    .1350736
    incomecat |  -.0756113   .0196553    -3.85   0.000    -.1141351   -.0370876
         educ |  -.0717055   .0528558    -1.36   0.175     -.175301      .03189
    notbornus |  -.7873472   .4130401    -1.91   0.057    -1.596891    .0221965
parents_not~s |  -.0316406   .1411198    -0.22   0.823    -.3082303     .244949
         sr2k |  -.5969747   .2493732    -2.39   0.017    -1.085737   -.1082123
        _cons |   .6068169   .2195939     2.76   0.006     .1764208    1.037213
-------------------------------------------------------------------------------

. est sto bornus_h3ab

. logit benefit_bornus i.treatgroup##c.ziphisppct2017s immigscale $controls i.st
> atenum, vce(cluster statenum)

note: 1.statenum != 0 predicts failure perfectly
      1.statenum dropped and 1 obs not used

note: 9.statenum != 0 predicts failure perfectly
      9.statenum dropped and 8 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1187.2946  
Iteration 1:   log pseudolikelihood = -1124.5595  
Iteration 2:   log pseudolikelihood = -1123.0995  
Iteration 3:   log pseudolikelihood =  -1123.095  
Iteration 4:   log pseudolikelihood =  -1123.095  

Logistic regression                             Number of obs     =      2,019
                                                Wald chi2(17)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood =  -1123.095               Pseudo R2         =     0.0541

                               (Std. Err. adjusted for 49 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_bor~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.2430393   .1790173    -1.36   0.175    -.5939069    .1078282
Fiscal thr..  |  -.1932629   .1762211    -1.10   0.273    -.5386499    .1521241
              |
ziphisppct2~s |   .0178774   .0932265     0.19   0.848    -.1648432    .2005979
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |  -.1026412   .1464231    -0.70   0.483    -.3896251    .1843427
Fiscal thr..  |     .01756   .1225242     0.14   0.886     -.222583    .2577031
              |
   immigscale |   -.063473   .3131295    -0.20   0.839    -.6771955    .5502495
        black |   .0347646    .148182     0.23   0.815    -.2556667     .325196
     hispanic |   .1360431    .171604     0.79   0.428    -.2002945    .4723807
    otherrace |   .2818615   .1820556     1.55   0.122    -.0749608    .6386839
         male |  -.1698146   .0950318    -1.79   0.074    -.3560734    .0164442
       ideo01 |   -.621043    .280243    -2.22   0.027    -1.170309   -.0717768
         pid7 |  -.0388856   .0339404    -1.15   0.252    -.1054075    .0276364
       agecat |   .0343627   .0564493     0.61   0.543     -.076276    .1450013
    incomecat |   -.075048   .0214314    -3.50   0.000    -.1170528   -.0330432
         educ |  -.0833603   .0548249    -1.52   0.128    -.1908151    .0240945
    notbornus |  -.7237793   .4176133    -1.73   0.083    -1.542286    .0947276
parents_not~s |  -.0356031   .1457941    -0.24   0.807    -.3213542    .2501481
         sr2k |  -.5550101   .2487941    -2.23   0.026    -1.042638   -.0673827
              |
     statenum |
          AK  |          0  (empty)
          AL  |   -1.25124   .1371159    -9.13   0.000    -1.519982   -.9824982
          AR  |  -.5631134   .1061805    -5.30   0.000    -.7712233   -.3550035
          AZ  |  -.7255128   .1321234    -5.49   0.000    -.9844699   -.4665556
          CA  |  -1.200638   .1654201    -7.26   0.000    -1.524856    -.876421
          CO  |  -.0884358   .1238217    -0.71   0.475    -.3311218    .1542501
          CT  |  -1.973248   .1459855   -13.52   0.000    -2.259374   -1.687122
          DC  |  -1.037517   .1477899    -7.02   0.000     -1.32718   -.7478544
          DE  |          0  (empty)
          FL  |  -.8472111   .1226877    -6.91   0.000    -1.087675   -.6067476
          GA  |  -.9756087   .1485705    -6.57   0.000    -1.266801   -.6844159
          HI  |   .2655129   .2037347     1.30   0.192    -.1337998    .6648255
          IA  |  -.8916443   .1593954    -5.59   0.000    -1.204054    -.579235
          ID  |  -1.374623   .0935169   -14.70   0.000    -1.557913   -1.191333
          IL  |  -.9127789   .1261178    -7.24   0.000    -1.159965   -.6655925
          IN  |  -.9121754   .1137901    -8.02   0.000      -1.1352   -.6891509
          KS  |  -.7208638   .1362675    -5.29   0.000    -.9879433   -.4537844
          KY  |   -.782857   .1355952    -5.77   0.000    -1.048619   -.5170952
          LA  |  -.7970651   .1387783    -5.74   0.000    -1.069065   -.5250647
          MA  |  -1.341224   .1454773    -9.22   0.000    -1.626354   -1.056094
          MD  |  -1.083304   .1452509    -7.46   0.000     -1.36799   -.7986173
          ME  |  -.9903768   .1310616    -7.56   0.000    -1.247253   -.7335007
          MI  |  -.3840506   .1161097    -3.31   0.001    -.6116215   -.1564797
          MN  |  -1.076883    .113187    -9.51   0.000    -1.298726   -.8550408
          MO  |  -1.331674   .1012542   -13.15   0.000    -1.530129    -1.13322
          MS  |  -.7544734   .1367016    -5.52   0.000    -1.022404   -.4865432
          MT  |  -.8222377   .1645858    -5.00   0.000     -1.14482   -.4996554
          NC  |  -.8234844   .1155993    -7.12   0.000    -1.050055   -.5969139
          ND  |  -.0475456   .1845007    -0.26   0.797    -.4091604    .3140691
          NE  |   .3752146   .1362684     2.75   0.006     .1081334    .6422958
          NH  |  -1.287164   .1347749    -9.55   0.000    -1.551318    -1.02301
          NJ  |  -1.081953   .1495079    -7.24   0.000    -1.374983   -.7889227
          NM  |  -.7513547   .1681081    -4.47   0.000     -1.08084   -.4218689
          NV  |  -.5067801   .1261719    -4.02   0.000    -.7540724   -.2594878
          NY  |   -.741299   .1240589    -5.98   0.000    -.9844499   -.4981481
          OH  |  -.3749955   .1167575    -3.21   0.001     -.603836   -.1461549
          OK  |   -.499236   .1348555    -3.70   0.000    -.7635479   -.2349242
          OR  |    -.00468   .1025824    -0.05   0.964    -.2057377    .1963778
          PA  |  -.7304539   .1277767    -5.72   0.000    -.9808916   -.4800162
          RI  |  -.6825748   .1760702    -3.88   0.000    -1.027666   -.3374835
          SC  |  -1.041707   .1401106    -7.43   0.000    -1.316319   -.7670956
          SD  |  -.1119598   .1099896    -1.02   0.309    -.3275355    .1036159
          TN  |  -.7907111   .1163576    -6.80   0.000    -1.018768   -.5626544
          TX  |  -.9719977   .1494124    -6.51   0.000    -1.264841   -.6791548
          UT  |  -.2727024   .1204146    -2.26   0.024    -.5087106   -.0366941
          VA  |  -.3981249   .1370953    -2.90   0.004    -.6668269    -.129423
          VT  |  -.4253066   .1580513    -2.69   0.007    -.7350814   -.1155317
          WA  |  -.3092892   .1128124    -2.74   0.006    -.5303976   -.0881809
          WI  |  -.3078953   .1106131    -2.78   0.005     -.524693   -.0910976
          WV  |  -.4113608   .1365873    -3.01   0.003     -.679067   -.1436547
          WY  |          0  (omitted)
              |
        _cons |   1.348849    .307212     4.39   0.000     .7467241    1.950973
-------------------------------------------------------------------------------

. est sto bornus_h4ab

. esttab bornus_h12 bornus_h3ab bornus_h4ab using TableE2, replace label wrap va
> rwidth(25) cells("b(star fmt(2)) se(fmt(2) par)") onecell star(* .1 ** .05 ***
>  .01) scalars("N Observations" "r2_p Pseudo R-squared") title(Table E.2. Logit
>  Models of the Assumption that the Described Policy will Benefit Native-Born A
> mericans, with Interaction Terms) nobaselevels eqlabels(none) interaction(" X 
> ") substitute(=1 "") order(2.treatgroup 3.treatgroup immigscale 2.treatgroup#c
> .immigscale 3.treatgroup#c.immigscale ziphisppct2017s 2.treatgroup#c.ziphisppc
> t2017s 3.treatgroup#c.ziphisppct2017s) indicate("State fixed effects=*statenum
> " ) nomtitles collabels(none) addnotes("State-clustered standard errors in par
> entheses" "Hispanic population percentage measured in standard deviations" "* 
> p<0.1, ** p<0.05, *** p<.01") nolz 
(output written to TableE2.txt)

. 
. *Table E.3 - Multinomial
. mlogit whobencat i.treatgroup, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -2688.0706  
Iteration 1:   log pseudolikelihood = -2676.4994  
Iteration 2:   log pseudolikelihood = -2676.4575  
Iteration 3:   log pseudolikelihood = -2676.4575  

Multinomial logistic regression                 Number of obs     =      2,208
                                                Wald chi2(6)      =      32.28
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -2676.4575               Pseudo R2         =     0.0043

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
    whobencat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
Immigrants_~y |
   treatgroup |
Cultural t..  |  -.0656469   .1513202    -0.43   0.664     -.362229    .2309351
Fiscal thr..  |   .4051494   .1402678     2.89   0.004     .1302295    .6800692
              |
        _cons |  -.9834853   .0875205   -11.24   0.000    -1.155022   -.8119483
--------------+----------------------------------------------------------------
Immigrants_~S |
   treatgroup |
Cultural t..  |  -.1397119    .158239    -0.88   0.377    -.4498547    .1704309
Fiscal thr..  |   .1031124   .1476957     0.70   0.485    -.1863659    .3925907
              |
        _cons |  -1.222715    .106265   -11.51   0.000     -1.43099    -1.01444
--------------+----------------------------------------------------------------
Born_US_only  |
   treatgroup |
Cultural t..  |  -.4724176   .1864914    -2.53   0.011    -.8379341   -.1069012
Fiscal thr..  |  -.0865084    .163353    -0.53   0.596    -.4066744    .2336575
              |
        _cons |  -1.222715   .1371836    -8.91   0.000     -1.49159     -.95384
--------------+----------------------------------------------------------------
Neither       |  (base outcome)
-------------------------------------------------------------------------------

. est sto mlogit

. 
. esttab mlogit using TableE3, replace tab label wrap varwidth(25) b(2) se(2) st
> arlevels(* 0.1 ** 0.05 *** .01) scalars("N Observations" "r2_p Pseudo R-square
> d") title(Table E.3. Experimental Effects on Beneficiary Assumptions, Multinom
> ial Specification (Neither as Base Outcome)) nonumbers nobaselevels unstack no
> omitted substitute(=1 "" _ " " "Immigrants    born US" "Immigrants & born US")
>  legend
(output written to TableE3.txt)

. 
. *Table E.4 - Race Interactions
. global controlsnorace "male ideo01 pid7 agecat incomecat educ notbornus parent
> s_notbornus sr2k"

. logit benefit_immigonly i.treatgroup##i.white, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1143.1735  
Iteration 1:   log pseudolikelihood = -1127.7718  
Iteration 2:   log pseudolikelihood = -1127.5718  
Iteration 3:   log pseudolikelihood = -1127.5718  

Logistic regression                             Number of obs     =      2,192
                                                Wald chi2(5)      =      38.94
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1127.5718               Pseudo R2         =     0.0136

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .0593365   .2589966     0.23   0.819    -.4482874    .5669605
Fiscal thr..  |  -.0256212   .2770497    -0.09   0.926    -.5686286    .5173862
              |
      1.white |   .3268852   .1987841     1.64   0.100    -.0627246    .7164949
              |
   treatgroup#|
        white |
Cultural t.. #|
           1  |  -.0496778    .270018    -0.18   0.854    -.5789033    .4795478
Fiscal thr.. #|
           1  |   .5122283   .2974248     1.72   0.085    -.0707137     1.09517
              |
        _cons |  -1.689977   .1795491    -9.41   0.000    -2.041886   -1.338067
-------------------------------------------------------------------------------

. est sto h12r

. logit benefit_immigonly i.treatgroup##i.white##c.immigscale, vce(cluster state
> num) 

Iteration 0:   log pseudolikelihood = -1107.5312  
Iteration 1:   log pseudolikelihood = -1083.7897  
Iteration 2:   log pseudolikelihood = -1083.2389  
Iteration 3:   log pseudolikelihood = -1083.2385  
Iteration 4:   log pseudolikelihood = -1083.2385  

Logistic regression                             Number of obs     =      2,125
                                                Wald chi2(11)     =      95.62
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1083.2385               Pseudo R2         =     0.0219

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .2362552   .6877522     0.34   0.731    -1.111714    1.584225
Fiscal thr..  |   .6256481   .8080243     0.77   0.439    -.9580504    2.209347
              |
      1.white |  -.3784091   .6385985    -0.59   0.553    -1.630039     .873221
              |
   treatgroup#|
        white |
Cultural t.. #|
           1  |  -.2365024   .9000916    -0.26   0.793     -2.00065    1.527645
Fiscal thr.. #|
           1  |  -.1134609   .9920757    -0.11   0.909    -2.057894    1.830972
              |
   immigscale |  -.3766029   1.181993    -0.32   0.750    -2.693267    1.940061
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |  -.4535236   1.630284    -0.28   0.781    -3.648822    2.741775
Fiscal thr..  |  -1.479581   1.758768    -0.84   0.400    -4.926703    1.967541
              |
        white#|
 c.immigscale |
           1  |   1.441998   1.385094     1.04   0.298    -1.272736    4.156733
              |
   treatgroup#|
        white#|
 c.immigscale |
Cultural t.. #|
           1  |   .4209265   1.868825     0.23   0.822    -3.241902    4.083755
Fiscal thr.. #|
           1  |   1.370834   2.003062     0.68   0.494    -2.555095    5.296764
              |
        _cons |  -1.553657   .5296596    -2.93   0.003    -2.591771   -.5155433
-------------------------------------------------------------------------------

. est sto h3abr

. logit benefit_immigonly i.treatgroup##i.white##c.immigscale ziphisppct2017s $c
> ontrolsnorace, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1068.3668  
Iteration 1:   log pseudolikelihood = -1002.3368  
Iteration 2:   log pseudolikelihood = -1000.1356  
Iteration 3:   log pseudolikelihood = -1000.1301  
Iteration 4:   log pseudolikelihood = -1000.1301  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(21)     =     433.13
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1000.1301               Pseudo R2         =     0.0639

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .0322802   .6708344     0.05   0.962    -1.282531    1.347091
Fiscal thr..  |   .6045631   .8367969     0.72   0.470    -1.035529    2.244655
              |
      1.white |  -.8344636   .6571228    -1.27   0.204    -2.122401    .4534734
              |
   treatgroup#|
        white |
Cultural t.. #|
           1  |  -.0546415   .9149288    -0.06   0.952    -1.847869    1.738586
Fiscal thr.. #|
           1  |  -.0384893   .9768693    -0.04   0.969    -1.953118    1.876139
              |
   immigscale |  -1.208317   1.252525    -0.96   0.335     -3.66322    1.246586
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |  -.1689178   1.582211    -0.11   0.915    -3.269995    2.932159
Fiscal thr..  |  -1.400075   1.826092    -0.77   0.443    -4.979149       2.179
              |
        white#|
 c.immigscale |
           1  |   1.820806   1.413628     1.29   0.198    -.9498547    4.591466
              |
   treatgroup#|
        white#|
 c.immigscale |
Cultural t.. #|
           1  |   .1794743   1.855225     0.10   0.923      -3.4567    3.815649
Fiscal thr.. #|
           1  |   1.258934   2.002237     0.63   0.530    -2.665378    5.183245
              |
ziphisppct2~s |  -.0513757   .0469801    -1.09   0.274     -.143455    .0407035
         male |   .0942948   .1074524     0.88   0.380    -.1163081    .3048977
       ideo01 |   .6619741   .2965805     2.23   0.026      .080687    1.243261
         pid7 |   .0491506   .0391541     1.26   0.209    -.0275901    .1258912
       agecat |   .1152101   .0721738     1.60   0.110     -.026248    .2566682
    incomecat |    .077154   .0228933     3.37   0.001      .032284     .122024
         educ |   .1646409    .054974     2.99   0.003     .0568938     .272388
    notbornus |   .3912731   .4427101     0.88   0.377    -.4764227    1.258969
parents_not~s |   .1483717    .201509     0.74   0.462    -.2465787    .5433221
         sr2k |   .4470229   .2965535     1.51   0.132    -.1342112    1.028257
        _cons |  -2.963741   .5817898    -5.09   0.000    -4.104028   -1.823454
-------------------------------------------------------------------------------

. est sto h3abcontr

. 
. esttab h12r h3abr h3abcontr using TableE4, replace tab label wrap varwidth(25)
>  b(2) se(2) star(* 0.1 ** 0.05 *** .01) scalars("N Observations" "r2_p Pseudo 
> R-squared") title(Table E.4. Logit Models of the Assumption that the Described
>  Policy will Benefit Immigrants and Not Native-Born Americans, by Race, with S
> tate-Clustered Standard Errors) nobaselevels eqlabels(none) interaction(" X ")
>  substitute(=1 "" \% "%") order(2.treatgroup 3.treatgroup 1.white 2.treatgroup
> #1.white 3.treatgroup#1.white immigscale 2.treatgroup#c.immigscale 3.treatgrou
> p#c.immigscale  1.white#c.immigscale 2.treatgroup#1.white#c.immigscale 3.treat
> group#1.white#c.immigscale) indicate("Controls = $controlsnorace") nomtitles n
> olz 
(output written to TableE4.txt)

. 
. *Table E.5 - Race & Hispanic Population Interactions
. logit benefit_immigonly i.treatgroup##i.white##c.ziphisppct2017s, vce(cluster 
> statenum) 

Iteration 0:   log pseudolikelihood = -1112.7874  
Iteration 1:   log pseudolikelihood = -1094.3592  
Iteration 2:   log pseudolikelihood = -1093.8992  
Iteration 3:   log pseudolikelihood = -1093.8964  
Iteration 4:   log pseudolikelihood = -1093.8964  

Logistic regression                             Number of obs     =      2,110
                                                Wald chi2(11)     =      59.22
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1093.8964               Pseudo R2         =     0.0170

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.3602455    .358365    -1.01   0.315    -1.062628    .3421371
Fiscal thr..  |   .2083488   .4101291     0.51   0.611    -.5954894    1.012187
              |
      1.white |   .1810805   .3098023     0.58   0.559    -.4261208    .7882818
              |
   treatgroup#|
        white |
Cultural t.. #|
           1  |   .1839835   .3321585     0.55   0.580    -.4670352    .8350022
Fiscal thr.. #|
           1  |   .3534242   .3661768     0.97   0.334    -.3642691    1.071117
              |
ziphisppct2~s |  -.0998699   .1066095    -0.94   0.349    -.3088207    .1090808
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .3320736   .1278359     2.60   0.009     .0815198    .5826273
Fiscal thr..  |  -.2217498   .2087633    -1.06   0.288    -.6309182    .1874187
              |
        white#|
           c. |
ziphisppct2~s |
           1  |    .072067   .1551872     0.46   0.642    -.2320943    .3762283
              |
   treatgroup#|
        white#|
           c. |
ziphisppct2~s |
Cultural t.. #|
           1  |  -.0674868   .2047906    -0.33   0.742     -.468869    .3338953
Fiscal thr.. #|
           1  |    .102013   .2260448     0.45   0.652    -.3410267    .5450526
              |
        _cons |  -1.501988   .2915451    -5.15   0.000    -2.073406   -.9305702
-------------------------------------------------------------------------------

. est sto h4ab2017r

. logit benefit_immigonly i.treatgroup##i.white##c.ziphisppct2017s immigscale $c
> ontrolsnorace, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1068.3668  
Iteration 1:   log pseudolikelihood = -1002.4379  
Iteration 2:   log pseudolikelihood =  -1000.308  
Iteration 3:   log pseudolikelihood = -1000.2943  
Iteration 4:   log pseudolikelihood = -1000.2943  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(21)     =     461.67
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1000.2943               Pseudo R2         =     0.0637

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.3409864   .3707785    -0.92   0.358    -1.067699    .3857261
Fiscal thr..  |   .5305719   .4353518     1.22   0.223    -.3227018    1.383846
              |
      1.white |  -.0587019   .3132904    -0.19   0.851    -.6727399     .555336
              |
   treatgroup#|
        white |
Cultural t.. #|
           1  |   .1651713   .3541553     0.47   0.641    -.5289604    .8593031
Fiscal thr.. #|
           1  |    .042279   .3940505     0.11   0.915    -.7300457    .8146037
              |
ziphisppct2~s |  -.0759576    .118128    -0.64   0.520    -.3074843     .155569
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .2553456   .1795297     1.42   0.155    -.0965262    .6072174
Fiscal thr..  |  -.3591361    .228938    -1.57   0.117    -.8078463    .0895742
              |
        white#|
           c. |
ziphisppct2~s |
           1  |   .0296873   .1827714     0.16   0.871    -.3285381    .3879127
              |
   treatgroup#|
        white#|
           c. |
ziphisppct2~s |
Cultural t.. #|
           1  |  -.0035839   .2032429    -0.02   0.986    -.4019328    .3947649
Fiscal thr.. #|
           1  |    .254455   .2745158     0.93   0.354     -.283586     .792496
              |
   immigscale |   .2100397   .3129942     0.67   0.502    -.4034176    .8234971
         male |   .0990002   .1104355     0.90   0.370    -.1174494    .3154498
       ideo01 |   .7115079   .3072714     2.32   0.021      .109267    1.313749
         pid7 |   .0496558    .040446     1.23   0.220    -.0296169    .1289284
       agecat |    .114244   .0751876     1.52   0.129     -.033121    .2616089
    incomecat |   .0771801   .0227444     3.39   0.001     .0326019    .1217582
         educ |    .161589   .0559716     2.89   0.004     .0518868    .2712912
    notbornus |   .2824494   .4405611     0.64   0.521    -.5810345    1.145933
parents_not~s |   .1848064     .21091     0.88   0.381    -.2285697    .5981825
         sr2k |   .4621323   .3010822     1.53   0.125    -.1279779    1.052243
        _cons |  -3.557097   .3767614    -9.44   0.000    -4.295536   -2.818658
-------------------------------------------------------------------------------

. est sto h4abcont2017r

. logit benefit_immigonly i.treatgroup##i.white##c.ziphisppct2017s immigscale $c
> ontrolsnorace i.statenum, vce(cluster statenum)

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 26.statenum != 0 predicts failure perfectly
      26.statenum dropped and 13 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 40.statenum != 0 predicts failure perfectly
      40.statenum dropped and 6 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1061.6154  
Iteration 1:   log pseudolikelihood = -971.70519  
Iteration 2:   log pseudolikelihood = -967.39876  
Iteration 3:   log pseudolikelihood = -967.36572  
Iteration 4:   log pseudolikelihood = -967.36571  

Logistic regression                             Number of obs     =      2,006
                                                Wald chi2(21)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -967.36571               Pseudo R2         =     0.0888

                               (Std. Err. adjusted for 47 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.2635464   .3875574    -0.68   0.496    -1.023145    .4960521
Fiscal thr..  |   .6672307   .4751864     1.40   0.160    -.2641175    1.598579
              |
      1.white |   .0839527   .3370532     0.25   0.803    -.5766594    .7445648
              |
   treatgroup#|
        white |
Cultural t.. #|
           1  |   .0808031   .3596367     0.22   0.822    -.6240719    .7856782
Fiscal thr.. #|
           1  |  -.0639549   .4272872    -0.15   0.881    -.9014225    .7735127
              |
ziphisppct2~s |  -.0823481   .1328593    -0.62   0.535    -.3427474    .1780513
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |    .215678   .1803817     1.20   0.232    -.1378636    .5692196
Fiscal thr..  |  -.4286171   .2532455    -1.69   0.091    -.9249691     .067735
              |
        white#|
           c. |
ziphisppct2~s |
           1  |  -.0144823   .1888085    -0.08   0.939    -.3845402    .3555756
              |
   treatgroup#|
        white#|
           c. |
ziphisppct2~s |
Cultural t.. #|
           1  |   .0299141    .218244     0.14   0.891    -.3978362    .4576644
Fiscal thr.. #|
           1  |   .3254287   .2852973     1.14   0.254    -.2337437    .8846012
              |
   immigscale |   .3167739     .32623     0.97   0.332    -.3226252    .9561731
         male |   .0895291   .1203824     0.74   0.457    -.1464162    .3254743
       ideo01 |   .7096422   .3051204     2.33   0.020     .1116172    1.307667
         pid7 |   .0493888    .040532     1.22   0.223    -.0300525    .1288301
       agecat |    .087315   .0762584     1.14   0.252    -.0621487    .2367786
    incomecat |   .0773398   .0247794     3.12   0.002     .0287732    .1259065
         educ |   .1682366   .0574657     2.93   0.003     .0556059    .2808673
    notbornus |   .3405492   .4730029     0.72   0.472    -.5865194    1.267618
parents_not~s |   .1509223   .2113532     0.71   0.475    -.2633224     .565167
         sr2k |   .4232774   .3207408     1.32   0.187    -.2053631    1.051918
              |
     statenum |
          AK  |          0  (empty)
          AL  |   -1.97535     .13384   -14.76   0.000    -2.237671   -1.713028
          AR  |  -1.158734   .1127309   -10.28   0.000    -1.379683    -.937786
          AZ  |  -1.072432    .138244    -7.76   0.000    -1.343385   -.8014784
          CA  |  -1.083448   .1557491    -6.96   0.000    -1.388711   -.7781856
          CO  |  -1.094268   .1254293    -8.72   0.000    -1.340105   -.8484313
          CT  |  -.9548615   .1473864    -6.48   0.000    -1.243734   -.6659895
          DC  |  -.5920865   .1574596    -3.76   0.000    -.9007017   -.2834713
          DE  |  -.6309807   .1301753    -4.85   0.000    -.8861195   -.3758419
          FL  |   -1.31686   .1207394   -10.91   0.000    -1.553505   -1.080215
          GA  |  -.6542689   .1363637    -4.80   0.000    -.9215368    -.387001
          HI  |  -.0711987    .249009    -0.29   0.775    -.5592473    .4168499
          IA  |  -.8222172   .1478781    -5.56   0.000    -1.112053   -.5323814
          ID  |  -1.443932   .1254477   -11.51   0.000    -1.689805   -1.198059
          IL  |  -1.701094   .1227826   -13.85   0.000    -1.941744   -1.460445
          IN  |  -1.971243   .1199258   -16.44   0.000    -2.206294   -1.736193
          KS  |  -1.895842   .1519152   -12.48   0.000    -2.193591   -1.598094
          KY  |  -2.018519   .1354455   -14.90   0.000    -2.283987    -1.75305
          LA  |  -1.486028   .1590412    -9.34   0.000    -1.797743   -1.174313
          MA  |  -2.021995   .1566619   -12.91   0.000    -2.329046   -1.714943
          MD  |  -1.465008   .1398833   -10.47   0.000    -1.739174   -1.190841
          ME  |  -.6687163   .1269247    -5.27   0.000    -.9174841   -.4199486
          MI  |  -1.739727   .1282899   -13.56   0.000    -1.991171   -1.488284
          MN  |  -1.016394   .1360119    -7.47   0.000    -1.282973    -.749816
          MO  |  -1.092008    .110929    -9.84   0.000    -1.309424   -.8745906
          MS  |          0  (empty)
          MT  |  -1.485817   .1805101    -8.23   0.000     -1.83961   -1.132024
          NC  |  -1.433292   .1136178   -12.62   0.000    -1.655979   -1.210605
          ND  |          0  (empty)
          NE  |  -.6421679   .1690586    -3.80   0.000    -.9735167   -.3108191
          NH  |  -.0693063   .1538586    -0.45   0.652    -.3708636     .232251
          NJ  |  -1.167006   .1510586    -7.73   0.000    -1.463076   -.8709367
          NM  |  -1.801529   .1623305   -11.10   0.000    -2.119691   -1.483367
          NV  |  -2.231897   .1273694   -17.52   0.000    -2.481536   -1.982258
          NY  |  -1.579121   .1450182   -10.89   0.000    -1.863351   -1.294891
          OH  |  -2.028437   .1076222   -18.85   0.000    -2.239373   -1.817502
          OK  |  -2.659472   .1475455   -18.02   0.000    -2.948656   -2.370288
          OR  |  -1.818148   .1556622   -11.68   0.000     -2.12324   -1.513055
          PA  |  -1.472542   .1229737   -11.97   0.000    -1.713566   -1.231518
          RI  |          0  (empty)
          SC  |  -1.717513   .1129947   -15.20   0.000    -1.938978   -1.496047
          SD  |  -1.758333   .1287594   -13.66   0.000    -2.010696   -1.505969
          TN  |  -1.173534   .1010268   -11.62   0.000    -1.371543   -.9755251
          TX  |  -1.419129   .1371209   -10.35   0.000    -1.687881   -1.150377
          UT  |  -3.031435   .1256505   -24.13   0.000    -3.277706   -2.785165
          VA  |  -1.235289   .1315323    -9.39   0.000    -1.493088   -.9774905
          VT  |  -1.638514   .1551531   -10.56   0.000    -1.942609    -1.33442
          WA  |   -1.55513   .1353926   -11.49   0.000    -1.820495   -1.289765
          WI  |  -1.428291   .1179058   -12.11   0.000    -1.659382     -1.1972
          WV  |  -1.911657   .1343745   -14.23   0.000    -2.175026   -1.648287
          WY  |          0  (omitted)
              |
        _cons |  -2.208287   .4850897    -4.55   0.000    -3.159045   -1.257528
-------------------------------------------------------------------------------

. est sto h4abcontfe2017r

. 
. esttab h4ab2017r h4abcont2017r h4abcontfe2017r using TableE5, replace tab labe
> l wrap varwidth(25) b(2) se(2) star(* 0.1 ** 0.05 *** .01) scalars("N Observat
> ions" "r2_p Pseudo R-squared") title(Table E.5. Logit Models of the Assumption
>  that the Described Policy will Benefit Immigrants and Not Native-Born America
> ns, by Race, with State-Clustered Standard Errors) nobaselevels eqlabels(none)
>  interaction(" X ") substitute(=1 "" "\% Hispanic population in ZIP code" "His
> panic population") order(2.treatgroup 3.treatgroup 1.white ziphisppct2017s  2.
> treatgroup#1.white 3.treatgroup#1.white 2.treatgroup#c.ziphisppct2017s 3.treat
> group#c.ziphisppct2017s 1.white#c.ziphisppct2017s 2.treatgroup#1.white#c.ziphi
> sppct2017s 3.treatgroup#1.white#c.ziphisppct2017s immigscale) indicate("Contro
> ls = $controlsnorace" "State fixed effects=*statenum") nomtitles nolz addnote(
> Hispanic population percentage measured in standard deviations)
(output written to TableE5.txt)

. 
. *Table E.6 - National Origin Interactions
. global controlsnonat "black hispanic otherrace male ideo01 pid7 agecat incomec
> at educ sr2k"

. logit benefit_immigonly i.treatgroup##i.selfparents_notbornus, vce(cluster sta
> tenum)

Iteration 0:   log pseudolikelihood = -1140.6677  
Iteration 1:   log pseudolikelihood = -1132.2759  
Iteration 2:   log pseudolikelihood = -1132.1417  
Iteration 3:   log pseudolikelihood = -1132.1417  

Logistic regression                             Number of obs     =      2,187
                                                Wald chi2(5)      =      18.95
                                                Prob > chi2       =     0.0020
Log pseudolikelihood = -1132.1417               Pseudo R2         =     0.0075

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .0596584   .1623183     0.37   0.713    -.2584797    .3777965
Fiscal thr..  |   .3632194   .1544088     2.35   0.019     .0605837    .6658552
              |
1.selfparen~s |   .0915117    .218385     0.42   0.675    -.3365149    .5195384
              |
   treatgroup#|
selfparents~s |
Cultural t.. #|
           1  |   -.275976   .4043427    -0.68   0.495    -1.068473    .5165211
Fiscal thr.. #|
           1  |   .3425153   .3200934     1.07   0.285    -.2848562    .9698868
              |
        _cons |  -1.457603   .1043658   -13.97   0.000    -1.662157    -1.25305
-------------------------------------------------------------------------------

. est sto h12n

. logit benefit_immigonly i.treatgroup##i.selfparents_notbornus##c.immigscale, v
> ce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1107.5312  
Iteration 1:   log pseudolikelihood = -1088.0351  
Iteration 2:   log pseudolikelihood = -1087.6655  
Iteration 3:   log pseudolikelihood = -1087.6653  
Iteration 4:   log pseudolikelihood = -1087.6653  

Logistic regression                             Number of obs     =      2,125
                                                Wald chi2(11)     =      71.37
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1087.6653               Pseudo R2         =     0.0179

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1413103   .3569746    -0.40   0.692    -.8409677    .5583471
Fiscal thr..  |   .0811491    .310486     0.26   0.794    -.5273923    .6896905
              |
1.selfparen~s |  -.4533651   .7220331    -0.63   0.530    -1.868524    .9617938
              |
   treatgroup#|
selfparents~s |
Cultural t.. #|
           1  |   1.451022   .8779811     1.65   0.098    -.2697898    3.171833
Fiscal thr.. #|
           1  |   1.496251   1.118477     1.34   0.181    -.6959243    3.688426
              |
   immigscale |   .7450765    .430184     1.73   0.083    -.0980686    1.588222
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |    .319111   .5331707     0.60   0.549    -.7258844    1.364106
Fiscal thr..  |   .4717903   .5313423     0.89   0.375    -.5696214    1.513202
              |
selfparents~s#|
 c.immigscale |
           1  |   1.254259   1.300074     0.96   0.335    -1.293839    3.802358
              |
   treatgroup#|
selfparents~s#|
 c.immigscale |
Cultural t.. #|
           1  |  -3.785037   2.221301    -1.70   0.088    -8.138707    .5686335
Fiscal thr.. #|
           1  |  -2.292492   2.123504    -1.08   0.280    -6.454483    1.869499
              |
        _cons |  -1.840338   .2491915    -7.39   0.000    -2.328745   -1.351932
-------------------------------------------------------------------------------

. est sto h3abn

. logit benefit_immigonly i.treatgroup##i.selfparents_notbornus##c.immigscale zi
> phisppct2017s $controlsnonat, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1068.3668  
Iteration 1:   log pseudolikelihood = -1003.4464  
Iteration 2:   log pseudolikelihood = -1001.4152  
Iteration 3:   log pseudolikelihood = -1001.4114  
Iteration 4:   log pseudolikelihood = -1001.4114  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(22)     =     330.37
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1001.4114               Pseudo R2         =     0.0627

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1495698   .3714064    -0.40   0.687     -.877513    .5783734
Fiscal thr..  |   .1191167   .3360862     0.35   0.723    -.5396002    .7778335
              |
1.selfparen~s |   .0076187   .8727934     0.01   0.993    -1.703025    1.718262
              |
   treatgroup#|
selfparents~s |
Cultural t.. #|
           1  |   1.058519   .9254482     1.14   0.253     -.755326    2.872364
Fiscal thr.. #|
           1  |   1.406848    1.29431     1.09   0.277    -1.129953    3.943649
              |
   immigscale |   .1324564   .5278643     0.25   0.802    -.9021386    1.167051
              |
   treatgroup#|
 c.immigscale |
Cultural t..  |   .2851247   .5927499     0.48   0.631    -.8766437    1.446893
Fiscal thr..  |   .4084408   .5998911     0.68   0.496    -.7673241    1.584206
              |
selfparents~s#|
 c.immigscale |
           1  |   .4185747   1.578908     0.27   0.791    -2.676028    3.513178
              |
   treatgroup#|
selfparents~s#|
 c.immigscale |
Cultural t.. #|
           1  |  -2.935933   2.390652    -1.23   0.219    -7.621524    1.749658
Fiscal thr.. #|
           1  |  -1.919274   2.376083    -0.81   0.419    -6.576312    2.737764
              |
ziphisppct2~s |  -.0613931   .0465116    -1.32   0.187    -.1525541     .029768
        black |  -.0004129   .2488664    -0.00   0.999     -.488182    .4873562
     hispanic |   .0325336   .1632942     0.20   0.842    -.2875172    .3525843
    otherrace |  -.4090868   .2100413    -1.95   0.051    -.8207603    .0025866
         male |   .1144702   .1089681     1.05   0.293    -.0991034    .3280438
       ideo01 |   .7124774   .2966675     2.40   0.016     .1310197    1.293935
         pid7 |   .0501784   .0386481     1.30   0.194    -.0255705    .1259273
       agecat |   .1265097    .075382     1.68   0.093    -.0212363    .2742558
    incomecat |   .0771211   .0230923     3.34   0.001     .0318611    .1223812
         educ |   .1645714   .0558723     2.95   0.003     .0550637    .2740792
         sr2k |   .4646808   .3073755     1.51   0.131    -.1377641    1.067126
        _cons |  -3.575319   .3251972   -10.99   0.000    -4.212694   -2.937945
-------------------------------------------------------------------------------

. est sto h3abcontn

. 
. esttab h12n h3abn h3abcontn using TableE6, replace tab label wrap varwidth(25)
>  b(2) se(2) star(* 0.1 ** 0.05 *** .01) scalars("N Observations" "r2_p Pseudo 
> R-squared") title(Table E.6. Logit Models of the Assumption that the Described
>  Policy will Benefit Immigrants and Not Native-Born Americans, by National Ori
> gin, with State-Clustered Standard Errors) nobaselevels eqlabels(none) interac
> tion(" X ") substitute(=1 "" "X Anti-immigration scale" "X Anti-immigration" \
> % "%") order(2.treatgroup 3.treatgroup 1.selfparents_notbornus 2.treatgroup#1.
> selfparents_notbornus 3.treatgroup#1.selfparents_notbornus immigscale 2.treatg
> roup#c.immigscale 3.treatgroup#c.immigscale  1.selfparents_notbornus#c.immigsc
> ale 2.treatgroup#1.selfparents_notbornus#c.immigscale 3.treatgroup#1.selfparen
> ts_notbornus#c.immigscale) indicate("Controls = $controlsnonat") nomtitles nol
> z 
(output written to TableE6.txt)

. 
. *Table E.7 - National Origin & Hispanic Population Interaction 
. logit benefit_immigonly i.treatgroup##i.selfparents_notbornus##c.ziphisppct201
> 7s, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1110.2792  
Iteration 1:   log pseudolikelihood =  -1094.465  
Iteration 2:   log pseudolikelihood = -1094.1372  
Iteration 3:   log pseudolikelihood =  -1094.137  
Iteration 4:   log pseudolikelihood =  -1094.137  

Logistic regression                             Number of obs     =      2,105
                                                Wald chi2(11)     =      58.05
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -1094.137               Pseudo R2         =     0.0145

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1021597   .2002332    -0.51   0.610    -.4946095    .2902902
Fiscal thr..  |   .5428414   .1731802     3.13   0.002     .2034144    .8822685
              |
1.selfparen~s |   .2082281   .2902184     0.72   0.473    -.3605896    .7770458
              |
   treatgroup#|
selfparents~s |
Cultural t.. #|
           1  |  -.8011848   .6377848    -1.26   0.209     -2.05122    .4488504
Fiscal thr.. #|
           1  |   .1671466    .382643     0.44   0.662    -.5828198    .9171131
              |
ziphisppct2~s |  -.0826363   .0795914    -1.04   0.299    -.2386325      .07336
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .2229213   .1633495     1.36   0.172    -.0972379    .5430805
Fiscal thr..  |  -.2637692   .1350278    -1.95   0.051    -.5284188    .0008805
              |
selfparents~s#|
           c. |
ziphisppct2~s |
           1  |  -.0178719   .1559441    -0.11   0.909    -.3235167    .2877728
              |
   treatgroup#|
selfparents~s#|
           c. |
ziphisppct2~s |
Cultural t.. #|
           1  |   .2211833   .2325757     0.95   0.342    -.2346566    .6770232
Fiscal thr.. #|
           1  |   .2485834   .3088697     0.80   0.421    -.3567902    .8539569
              |
        _cons |  -1.367372   .1112718   -12.29   0.000     -1.58546   -1.149283
-------------------------------------------------------------------------------

. est sto h4ab2017n

. logit benefit_immigonly i.treatgroup##i.selfparents_notbornus##c.ziphisppct201
> 7s immigscale $controlsnonat, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1068.3668  
Iteration 1:   log pseudolikelihood = -998.63119  
Iteration 2:   log pseudolikelihood = -996.38077  
Iteration 3:   log pseudolikelihood = -996.37573  
Iteration 4:   log pseudolikelihood = -996.37573  

Logistic regression                             Number of obs     =      2,028
                                                Wald chi2(22)     =     418.26
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -996.37573               Pseudo R2         =     0.0674

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1706102   .1728166    -0.99   0.324    -.5093246    .1681041
Fiscal thr..  |   .5459831   .1767335     3.09   0.002     .1995918    .8923744
              |
1.selfparen~s |   .0737348   .3366879     0.22   0.827    -.5861613    .7336309
              |
   treatgroup#|
selfparents~s |
Cultural t.. #|
           1  |  -.6056828   .6706604    -0.90   0.366    -1.920153    .7087873
Fiscal thr.. #|
           1  |   .4795336   .4903898     0.98   0.328    -.4816127     1.44068
              |
ziphisppct2~s |  -.0793168   .0865403    -0.92   0.359    -.2489327    .0902991
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |    .248802   .1616541     1.54   0.124    -.0680343    .5656383
Fiscal thr..  |  -.2730676   .1557822    -1.75   0.080    -.5783951      .03226
              |
selfparents~s#|
           c. |
ziphisppct2~s |
           1  |   .0958663   .1535355     0.62   0.532    -.2050578    .3967904
              |
   treatgroup#|
selfparents~s#|
           c. |
ziphisppct2~s |
Cultural t.. #|
           1  |   .1560554   .2608075     0.60   0.550     -.355118    .6672287
Fiscal thr.. #|
           1  |    .162368   .3611003     0.45   0.653    -.5453756    .8701115
              |
   immigscale |   .2534524   .3138084     0.81   0.419    -.3616008    .8685056
        black |  -.0239486   .2511242    -0.10   0.924     -.516143    .4682458
     hispanic |   .0525318    .165594     0.32   0.751    -.2720265    .3770902
    otherrace |  -.3649101   .2198423    -1.66   0.097     -.795793    .0659729
         male |   .1165221   .1093961     1.07   0.287    -.0978903    .3309345
       ideo01 |   .7290267   .3108942     2.34   0.019     .1196853    1.338368
         pid7 |   .0454589   .0391053     1.16   0.245     -.031186    .1221038
       agecat |   .1241598    .073608     1.69   0.092    -.0201092    .2684288
    incomecat |   .0799747   .0233527     3.42   0.001     .0342043    .1257452
         educ |   .1654045   .0586151     2.82   0.005      .050521     .280288
         sr2k |   .4713303   .3207156     1.47   0.142    -.1572606    1.099921
        _cons |  -3.630932   .2730051   -13.30   0.000    -4.166012   -3.095852
-------------------------------------------------------------------------------

. est sto h4abcont2017n

. logit benefit_immigonly i.treatgroup##i.selfparents_notbornus##c.ziphisppct201
> 7s immigscale $controlsnonat i.statenum, vce(cluster statenum)

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 26.statenum != 0 predicts failure perfectly
      26.statenum dropped and 13 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 40.statenum != 0 predicts failure perfectly
      40.statenum dropped and 6 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1061.6154  
Iteration 1:   log pseudolikelihood =  -968.0545  
Iteration 2:   log pseudolikelihood = -963.54194  
Iteration 3:   log pseudolikelihood = -963.51223  
Iteration 4:   log pseudolikelihood = -963.51221  

Logistic regression                             Number of obs     =      2,006
                                                Wald chi2(22)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -963.51221               Pseudo R2         =     0.0924

                               (Std. Err. adjusted for 47 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   -.170126   .1968613    -0.86   0.387    -.5559671    .2157152
Fiscal thr..  |   .5807289   .1983887     2.93   0.003     .1918941    .9695637
              |
1.selfparen~s |  -.0188814   .3786457    -0.05   0.960    -.7610133    .7232506
              |
   treatgroup#|
selfparents~s |
Cultural t.. #|
           1  |  -.5778061   .7021672    -0.82   0.411    -1.954028    .7984162
Fiscal thr.. #|
           1  |   .5599989   .5365835     1.04   0.297    -.4916854    1.611683
              |
ziphisppct2~s |  -.1350266   .0777356    -1.74   0.082    -.2873857    .0173324
              |
   treatgroup#|
           c. |
ziphisppct2~s |
Cultural t..  |   .2356648   .1674246     1.41   0.159    -.0924815     .563811
Fiscal thr..  |  -.2824525   .1664513    -1.70   0.090    -.6086911    .0437861
              |
selfparents~s#|
           c. |
ziphisppct2~s |
           1  |   .1301998   .1645096     0.79   0.429    -.1922332    .4526327
              |
   treatgroup#|
selfparents~s#|
           c. |
ziphisppct2~s |
Cultural t.. #|
           1  |   .1565201   .2651372     0.59   0.555    -.3631392    .6761794
Fiscal thr.. #|
           1  |   .1262367   .3770839     0.33   0.738    -.6128341    .8653075
              |
   immigscale |   .3586917   .3251276     1.10   0.270    -.2785467      .99593
        black |   -.131659   .2706822    -0.49   0.627    -.6621864    .3988683
     hispanic |   .0321484   .1689991     0.19   0.849    -.2990838    .3633806
    otherrace |  -.4238072   .2297092    -1.84   0.065    -.8740289    .0264145
         male |   .1066834   .1186441     0.90   0.369    -.1258547    .3392216
       ideo01 |   .7335864   .3142819     2.33   0.020     .1176052    1.349568
         pid7 |   .0448397   .0390702     1.15   0.251    -.0317366    .1214159
       agecat |   .0986954   .0745037     1.32   0.185    -.0473292      .24472
    incomecat |   .0800474   .0253931     3.15   0.002     .0302779    .1298169
         educ |    .171285   .0597507     2.87   0.004     .0541758    .2883941
         sr2k |   .4292448   .3423244     1.25   0.210    -.2416987    1.100188
              |
     statenum |
          AK  |          0  (empty)
          AL  |  -1.963542   .1227134   -16.00   0.000    -2.204056   -1.723028
          AR  |   -1.15161   .1063128   -10.83   0.000    -1.359979   -.9432405
          AZ  |   -1.00541   .1417779    -7.09   0.000     -1.28329   -.7275308
          CA  |  -1.039043   .1538897    -6.75   0.000    -1.340661   -.7374247
          CO  |  -.9691806   .1296392    -7.48   0.000    -1.223269   -.7150925
          CT  |  -.9136822    .141748    -6.45   0.000    -1.191503   -.6358612
          DC  |  -.5500058   .1547133    -3.56   0.000    -.8532382   -.2467734
          DE  |  -.6020118   .1367373    -4.40   0.000    -.8700119   -.3340117
          FL  |  -1.262971   .1191959   -10.60   0.000    -1.496591   -1.029351
          GA  |  -.6284686   .1374512    -4.57   0.000     -.897868   -.3590691
          HI  |   .1509798   .3160603     0.48   0.633     -.468487    .7704467
          IA  |  -.8065274   .1294694    -6.23   0.000    -1.060283   -.5527721
          ID  |  -1.413453   .1222471   -11.56   0.000    -1.653053   -1.173853
          IL  |  -1.662659   .1143862   -14.54   0.000    -1.886852   -1.438466
          IN  |   -1.92135    .116599   -16.48   0.000     -2.14988    -1.69282
          KS  |  -1.808911   .1557649   -11.61   0.000    -2.114204   -1.503617
          KY  |  -2.010683   .1266168   -15.88   0.000    -2.258847   -1.762519
          LA  |  -1.443628   .1407353   -10.26   0.000    -1.719464   -1.167792
          MA  |  -1.940419   .1384422   -14.02   0.000    -2.211761   -1.669077
          MD  |  -1.428393   .1378691   -10.36   0.000    -1.698612   -1.158175
          ME  |  -.6093434   .1412424    -4.31   0.000    -.8861734   -.3325134
          MI  |  -1.713955   .1225739   -13.98   0.000    -1.954196   -1.473715
          MN  |  -.9989845   .1402412    -7.12   0.000    -1.273852   -.7241167
          MO  |  -1.050202   .1050416   -10.00   0.000     -1.25608   -.8443243
          MS  |          0  (empty)
          MT  |  -1.486767   .1928105    -7.71   0.000    -1.864669   -1.108865
          NC  |  -1.415891   .1052552   -13.45   0.000    -1.622187   -1.209594
          ND  |          0  (empty)
          NE  |  -.6937465   .1649321    -4.21   0.000    -1.017007   -.3704856
          NH  |   .0545468   .1431336     0.38   0.703      -.22599    .3350835
          NJ  |  -1.099244   .1481854    -7.42   0.000    -1.389682   -.8088062
          NM  |  -1.602658   .1896062    -8.45   0.000     -1.97428   -1.231037
          NV  |   -2.14718   .1295797   -16.57   0.000    -2.401152   -1.893209
          NY  |  -1.488435   .1356709   -10.97   0.000    -1.754345   -1.222525
          OH  |  -2.026949   .1026252   -19.75   0.000    -2.228091   -1.825807
          OK  |  -2.527509   .1440701   -17.54   0.000    -2.809882   -2.245137
          OR  |  -1.603649   .1382432   -11.60   0.000    -1.874601   -1.332697
          PA  |  -1.414865   .1187735   -11.91   0.000    -1.647657   -1.182073
          RI  |          0  (empty)
          SC  |  -1.721254   .1102273   -15.62   0.000    -1.937295   -1.505212
          SD  |  -1.904773   .1592851   -11.96   0.000    -2.216966   -1.592579
          TN  |  -1.128477   .0942226   -11.98   0.000     -1.31315   -.9438037
          TX  |  -1.364482   .1268435   -10.76   0.000    -1.613091   -1.115874
          UT  |  -3.057997   .1543779   -19.81   0.000    -3.360572   -2.755422
          VA  |   -1.17462   .1274452    -9.22   0.000    -1.424408   -.9248317
          VT  |  -1.647513   .1550078   -10.63   0.000    -1.951323   -1.343704
          WA  |  -1.509604   .1184904   -12.74   0.000    -1.741841   -1.277367
          WI  |  -1.395748   .1109724   -12.58   0.000     -1.61325   -1.178246
          WV  |  -1.919062   .1285817   -14.92   0.000    -2.171077   -1.667046
          WY  |          0  (omitted)
              |
        _cons |  -2.181032   .3495844    -6.24   0.000    -2.866205   -1.495859
-------------------------------------------------------------------------------

. est sto h4abcontfe2017n

. 
. esttab h4ab2017n h4abcont2017n h4abcontfe2017n using TableE7, replace tab labe
> l wrap varwidth(25) b(2) se(2) star(* 0.1 ** 0.05 *** .01) scalars("N Observat
> ions" "r2_p Pseudo R-squared") title(Table E.7 Logit Models of the Assumption 
> that the Described Policy will Benefit Immigrants and Not Native-Born American
> s, by National Origin, with State-Clustered Standard Errors) nobaselevels eqla
> bels(none) interaction(" X ") substitute(=1 "" "\% Hispanic population in ZIP 
> code" "Hispanic population") order(2.treatgroup 3.treatgroup 1.selfparents_not
> bornus ziphisppct2017s  2.treatgroup#1.selfparents_notbornus 3.treatgroup#1.se
> lfparents_notbornus 2.treatgroup#c.ziphisppct2017s 3.treatgroup#c.ziphisppct20
> 17s 1.selfparents_notbornus#c.ziphisppct2017s 2.treatgroup#1.selfparents_notbo
> rnus#c.ziphisppct2017s 3.treatgroup#1.selfparents_notbornus#c.ziphisppct2017s 
> immigscale) indicate("Controls = $controlsnonat" "State fixed effects=*statenu
> m") nomtitles nolz addnote(Hispanic population percentage measured in standard
>  deviations)
(output written to TableE7.txt)

. 
. *Table E.8 - Alternative ZIP Code Interaction Terms
. logit benefit_immigonly i.treatgroup##c.zipforeignpct2017s immigscale $control
> s i.statenum, vce(cluster statenum)

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 26.statenum != 0 predicts failure perfectly
      26.statenum dropped and 13 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 40.statenum != 0 predicts failure perfectly
      40.statenum dropped and 6 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1061.6154  
Iteration 1:   log pseudolikelihood =  -971.5586  
Iteration 2:   log pseudolikelihood = -967.53407  
Iteration 3:   log pseudolikelihood = -967.50795  
Iteration 4:   log pseudolikelihood = -967.50794  

Logistic regression                             Number of obs     =      2,006
                                                Wald chi2(18)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -967.50794               Pseudo R2         =     0.0886

                               (Std. Err. adjusted for 47 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.3857033   .2037468    -1.89   0.058    -.7850397    .0136332
Fiscal thr..  |   .5551117    .218253     2.54   0.011     .1273436    .9828798
              |
zipfore~2017s |  -.0035492   .0744324    -0.05   0.962     -.149434    .1423357
              |
   treatgroup#|
           c. |
zipfore~2017s |
Cultural t..  |   .3352347   .1124853     2.98   0.003     .1147674    .5557019
Fiscal thr..  |  -.0926254   .0956489    -0.97   0.333    -.2800938    .0948431
              |
   immigscale |   .3722899     .32884     1.13   0.258    -.2722246    1.016804
        black |  -.1905409   .2703999    -0.70   0.481    -.7205149    .3394332
     hispanic |  -.1551083   .1523382    -1.02   0.309    -.4536856     .143469
    otherrace |  -.4573854   .2086762    -2.19   0.028    -.8663833   -.0483875
         male |   .0797477   .1180099     0.68   0.499    -.1515474    .3110428
       ideo01 |   .7048298   .3020189     2.33   0.020     .1128835    1.296776
         pid7 |   .0512828   .0397994     1.29   0.198    -.0267225    .1292882
       agecat |   .0889269   .0748953     1.19   0.235    -.0578653     .235719
    incomecat |   .0770353   .0247749     3.11   0.002     .0284774    .1255933
         educ |   .1705571   .0575413     2.96   0.003     .0577782     .283336
    notbornus |   .3172442   .4218773     0.75   0.452    -.5096202    1.144109
parents_not~s |   .1839387   .1940846     0.95   0.343      -.19646    .5643375
         sr2k |   .3851906   .3383978     1.14   0.255    -.2780568    1.048438
              |
     statenum |
          AK  |          0  (empty)
          AL  |  -1.933936   .1165433   -16.59   0.000    -2.162356   -1.705515
          AR  |   -1.19476   .1145175   -10.43   0.000     -1.41921   -.9703099
          AZ  |   -1.20184   .1410171    -8.52   0.000    -1.478229   -.9254519
          CA  |  -1.328796   .1865909    -7.12   0.000    -1.694507   -.9630842
          CO  |  -1.206028   .1210808    -9.96   0.000    -1.443342   -.9687141
          CT  |   -1.04953   .1588247    -6.61   0.000    -1.360821   -.7382396
          DC  |  -.6578472   .1668285    -3.94   0.000    -.9848251   -.3308693
          DE  |  -.6922377    .119487    -5.79   0.000    -.9264279   -.4580475
          FL  |  -1.493687    .145816   -10.24   0.000    -1.779481   -1.207893
          GA  |  -.7084042   .1439369    -4.92   0.000    -.9905154    -.426293
          HI  |  -.0869747   .2424977    -0.36   0.720    -.5622614     .388312
          IA  |  -.8461273   .1424122    -5.94   0.000     -1.12525   -.5670044
          ID  |  -1.512071   .1268519   -11.92   0.000    -1.760696   -1.263446
          IL  |  -1.810307   .1357078   -13.34   0.000    -2.076289   -1.544324
          IN  |  -2.016879   .1252105   -16.11   0.000    -2.262287   -1.771471
          KS  |  -1.963739   .1461334   -13.44   0.000    -2.250155   -1.677323
          KY  |  -2.046404   .1270841   -16.10   0.000    -2.295484   -1.797324
          LA  |   -1.47459    .142677   -10.34   0.000    -1.754232   -1.194948
          MA  |  -2.115337   .1740709   -12.15   0.000    -2.456509   -1.774164
          MD  |   -1.52704   .1442211   -10.59   0.000    -1.809708   -1.244372
          ME  |   -.634655     .12621    -5.03   0.000    -.8820221   -.3872879
          MI  |  -1.782643   .1242333   -14.35   0.000    -2.026135    -1.53915
          MN  |  -1.133494   .1428626    -7.93   0.000    -1.413499   -.8534882
          MO  |  -1.087776   .1046753   -10.39   0.000    -1.292936   -.8826162
          MS  |          0  (empty)
          MT  |  -1.468311    .179431    -8.18   0.000    -1.819989   -1.116633
          NC  |  -1.494698   .1143189   -13.07   0.000    -1.718759   -1.270637
          ND  |          0  (empty)
          NE  |  -.6879065   .1672307    -4.11   0.000    -1.015673   -.3601402
          NH  |   -.094827   .1488297    -0.64   0.524    -.3865278    .1968739
          NJ  |  -1.343697   .1756252    -7.65   0.000    -1.687916   -.9994781
          NM  |   -1.93277   .1469352   -13.15   0.000    -2.220758   -1.644783
          NV  |  -2.402344   .1532196   -15.68   0.000    -2.702649   -2.102039
          NY  |  -1.698446   .1678691   -10.12   0.000    -2.027463   -1.369428
          OH  |  -2.055541   .1078374   -19.06   0.000    -2.266898   -1.844184
          OK  |   -2.67623   .1499413   -17.85   0.000    -2.970109    -2.38235
          OR  |  -1.861804   .1533066   -12.14   0.000    -2.162279   -1.561328
          PA  |  -1.514385   .1259369   -12.02   0.000    -1.761217   -1.267553
          RI  |          0  (empty)
          SC  |  -1.729097   .1066121   -16.22   0.000    -1.938053   -1.520141
          SD  |   -1.81334   .1392301   -13.02   0.000    -2.086226   -1.540454
          TN  |  -1.207665   .1026037   -11.77   0.000    -1.408764   -1.006565
          TX  |  -1.667489   .1437952   -11.60   0.000    -1.949322   -1.385656
          UT  |  -3.153762   .1358302   -23.22   0.000    -3.419984    -2.88754
          VA  |   -1.31468   .1441601    -9.12   0.000    -1.597229   -1.032132
          VT  |  -1.632486   .1565915   -10.43   0.000      -1.9394   -1.325572
          WA  |   -1.68233   .1485443   -11.33   0.000    -1.973472   -1.391189
          WI  |  -1.466482   .1190306   -12.32   0.000    -1.699778   -1.233187
          WV  |  -1.953131   .1276093   -15.31   0.000    -2.203241   -1.703022
          WY  |          0  (omitted)
              |
        _cons |  -2.069569   .3800553    -5.45   0.000    -2.814463   -1.324674
-------------------------------------------------------------------------------

. est sto foreign

. logit benefit_immigonly i.treatgroup##c.ziphispforeignpct2017s immigscale $con
> trols i.statenum, vce(cluster statenum) 

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 26.statenum != 0 predicts failure perfectly
      26.statenum dropped and 13 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 40.statenum != 0 predicts failure perfectly
      40.statenum dropped and 6 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1061.6154  
Iteration 1:   log pseudolikelihood = -973.85776  
Iteration 2:   log pseudolikelihood = -969.99177  
Iteration 3:   log pseudolikelihood = -969.96386  
Iteration 4:   log pseudolikelihood = -969.96385  

Logistic regression                             Number of obs     =      2,006
                                                Wald chi2(18)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -969.96385               Pseudo R2         =     0.0863

                               (Std. Err. adjusted for 47 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.1179983   .1738085    -0.68   0.497    -.4586568    .2226601
Fiscal thr..  |   .5549243   .2172439     2.55   0.011      .129134    .9807145
              |
ziphispfore~s |  -.0953719   .0794984    -1.20   0.230    -.2511858    .0604421
              |
   treatgroup#|
           c. |
ziphispfore~s |
Cultural t..  |   .1736932   .1302863     1.33   0.182    -.0816632    .4290495
Fiscal thr..  |  -.1562089   .1692649    -0.92   0.356    -.4879621    .1755443
              |
   immigscale |   .3715992   .3221953     1.15   0.249     -.259892    1.003091
        black |  -.1463348     .26829    -0.55   0.585    -.6721735    .3795038
     hispanic |  -.0185765   .1510122    -0.12   0.902     -.314555    .2774021
    otherrace |  -.4434481   .2166463    -2.05   0.041    -.8680671    -.018829
         male |   .0978111    .117145     0.83   0.404     -.131789    .3274111
       ideo01 |   .6892416   .3056468     2.26   0.024      .090185    1.288298
         pid7 |   .0503292   .0392275     1.28   0.199    -.0265552    .1272136
       agecat |   .0931567    .074764     1.25   0.213    -.0533779    .2396914
    incomecat |   .0771307   .0248192     3.11   0.002     .0284859    .1257754
         educ |   .1701103   .0578278     2.94   0.003     .0567698    .2834508
    notbornus |    .350454   .4619833     0.76   0.448    -.5550167    1.255925
parents_not~s |   .1906039   .1929698     0.99   0.323    -.1876098    .5688177
         sr2k |   .3817257   .3356436     1.14   0.255    -.2761236    1.039575
              |
     statenum |
          AK  |          0  (empty)
          AL  |  -1.960381   .1220326   -16.06   0.000     -2.19956   -1.721201
          AR  |  -1.149868   .1105795   -10.40   0.000      -1.3666   -.9331366
          AZ  |  -1.057652   .1255517    -8.42   0.000    -1.303729   -.8115755
          CA  |  -1.092295   .1466712    -7.45   0.000    -1.379766   -.8048249
          CO  |  -1.115706   .1139323    -9.79   0.000     -1.33901   -.8924032
          CT  |  -.9540614   .1452413    -6.57   0.000    -1.238729   -.6693938
          DC  |  -.5482314   .1609091    -3.41   0.001    -.8636074   -.2328554
          DE  |  -.6595602   .1194939    -5.52   0.000    -.8937639   -.4253565
          FL  |   -1.31213   .1154977   -11.36   0.000    -1.538501   -1.085758
          GA  |  -.6302345   .1367298    -4.61   0.000      -.89822    -.362249
          HI  |   .0994919   .2383807     0.42   0.676    -.3677256    .5667095
          IA  |  -.7988165   .1402925    -5.69   0.000    -1.073785   -.5238483
          ID  |  -1.410636   .1199382   -11.76   0.000    -1.645711   -1.175561
          IL  |  -1.677572   .1192456   -14.07   0.000    -1.911289   -1.443855
          IN  |  -1.964126   .1213764   -16.18   0.000    -2.202019   -1.726232
          KS  |  -1.866033   .1415154   -13.19   0.000    -2.143398   -1.588668
          KY  |  -1.995735   .1252408   -15.94   0.000    -2.241202   -1.750268
          LA  |  -1.470864   .1457469   -10.09   0.000    -1.756522   -1.185205
          MA  |  -2.009324   .1518039   -13.24   0.000    -2.306854   -1.711794
          MD  |  -1.425554   .1364625   -10.45   0.000    -1.693016   -1.158093
          ME  |  -.6518863   .1306825    -4.99   0.000    -.9080193   -.3957533
          MI  |   -1.73463   .1235652   -14.04   0.000    -1.976813   -1.492446
          MN  |  -.9936219   .1310115    -7.58   0.000      -1.2504   -.7368441
          MO  |   -1.08879   .1061738   -10.25   0.000    -1.296887   -.8806936
          MS  |          0  (empty)
          MT  |  -1.496532   .1916554    -7.81   0.000     -1.87217   -1.120895
          NC  |  -1.407852   .1057058   -13.32   0.000    -1.615031   -1.200672
          ND  |          0  (empty)
          NE  |  -.6511521   .1645892    -3.96   0.000    -.9737411   -.3285632
          NH  |  -.0275748   .1453604    -0.19   0.850     -.312476    .2573264
          NJ  |  -1.141851   .1440813    -7.93   0.000    -1.424246   -.8594572
          NM  |  -1.843542   .1401881   -13.15   0.000    -2.118306   -1.568779
          NV  |  -2.211076     .12659   -17.47   0.000    -2.459188   -1.962964
          NY  |  -1.555133   .1412299   -11.01   0.000    -1.831938   -1.278327
          OH  |   -2.02017   .1043169   -19.37   0.000    -2.224628   -1.815713
          OK  |  -2.584145   .1451823   -17.80   0.000    -2.868697   -2.299593
          OR  |  -1.752721   .1494568   -11.73   0.000    -2.045651   -1.459791
          PA  |  -1.453386   .1203794   -12.07   0.000    -1.689325   -1.217447
          RI  |          0  (empty)
          SC  |  -1.688652   .1049215   -16.09   0.000    -1.894294   -1.483009
          SD  |  -1.707026   .1260207   -13.55   0.000    -1.954022    -1.46003
          TN  |  -1.145473   .0973363   -11.77   0.000    -1.336248   -.9546971
          TX  |   -1.45482   .1237332   -11.76   0.000    -1.697332   -1.212307
          UT  |    -2.9912   .1206754   -24.79   0.000     -3.22772   -2.754681
          VA  |  -1.173492   .1267752    -9.26   0.000    -1.421967    -.925017
          VT  |  -1.650159   .1562364   -10.56   0.000    -1.956377   -1.343942
          WA  |  -1.551129   .1306733   -11.87   0.000    -1.807244   -1.295014
          WI  |  -1.406441   .1127128   -12.48   0.000    -1.627354   -1.185528
          WV  |  -1.897974   .1264214   -15.01   0.000    -2.145756   -1.650193
          WY  |          0  (omitted)
              |
        _cons |  -2.152656   .3572182    -6.03   0.000    -2.852791   -1.452521
-------------------------------------------------------------------------------

. est sto hispforeign

. logit benefit_immigonly i.treatgroup##c.ziphispgrowth1217s immigscale $control
> s i.statenum, vce(cluster statenum)

note: 1.statenum != 0 predicts success perfectly
      1.statenum dropped and 1 obs not used

note: 26.statenum != 0 predicts failure perfectly
      26.statenum dropped and 13 obs not used

note: 29.statenum != 0 predicts failure perfectly
      29.statenum dropped and 2 obs not used

note: 40.statenum != 0 predicts failure perfectly
      40.statenum dropped and 6 obs not used

note: 51.statenum omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1061.6154  
Iteration 1:   log pseudolikelihood = -975.85279  
Iteration 2:   log pseudolikelihood = -972.05547  
Iteration 3:   log pseudolikelihood =  -972.0272  
Iteration 4:   log pseudolikelihood = -972.02719  

Logistic regression                             Number of obs     =      2,006
                                                Wald chi2(18)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -972.02719               Pseudo R2         =     0.0844

                               (Std. Err. adjusted for 47 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.0712912   .1593113    -0.45   0.655    -.3835357    .2409533
Fiscal thr..  |   .4339144   .1455345     2.98   0.003     .1486721    .7191567
              |
ziphisp~1217s |  -.1461929   .1059398    -1.38   0.168    -.3538311    .0614453
              |
   treatgroup#|
           c. |
ziphisp~1217s |
Cultural t..  |   .2073303   .1243175     1.67   0.095    -.0363276    .4509882
Fiscal thr..  |   .0920341    .125351     0.73   0.463    -.1536495    .3377176
              |
   immigscale |     .36643   .3153306     1.16   0.245    -.2516067    .9844666
        black |  -.1607044   .2711997    -0.59   0.553     -.692246    .3708373
     hispanic |  -.1075017    .146031    -0.74   0.462    -.3937172    .1787138
    otherrace |  -.4893601   .2085065    -2.35   0.019    -.8980253   -.0806949
         male |   .0873272   .1132777     0.77   0.441     -.134693    .3093473
       ideo01 |   .7082609     .30684     2.31   0.021     .1068654    1.309656
         pid7 |   .0510355   .0381402     1.34   0.181     -.023718     .125789
       agecat |   .0937271   .0743528     1.26   0.207    -.0520017    .2394559
    incomecat |   .0777336    .024977     3.11   0.002     .0287796    .1266876
         educ |   .1672466    .055646     3.01   0.003     .0581825    .2763107
    notbornus |   .3901003   .4575424     0.85   0.394    -.5066664    1.286867
parents_not~s |   .1898377   .1914183     0.99   0.321    -.1853353    .5650107
         sr2k |   .3982557   .3260817     1.22   0.222    -.2408526    1.037364
              |
     statenum |
          AK  |          0  (empty)
          AL  |  -1.949522   .1451601   -13.43   0.000    -2.234031   -1.665014
          AR  |  -1.126835   .1122512   -10.04   0.000    -1.346844   -.9068271
          AZ  |  -1.051252   .1198109    -8.77   0.000    -1.286077   -.8164267
          CA  |  -1.160221   .1484415    -7.82   0.000    -1.451161   -.8692813
          CO  |  -1.121263   .1092948   -10.26   0.000    -1.335477   -.9070487
          CT  |  -.8858264   .1418492    -6.24   0.000    -1.163846   -.6078069
          DC  |   -.548944   .1616724    -3.40   0.001    -.8658161   -.2320719
          DE  |  -.6412294   .1266053    -5.06   0.000    -.8893712   -.3930876
          FL  |  -1.320691   .1117088   -11.82   0.000    -1.539636   -1.101746
          GA  |  -.6030615   .1407835    -4.28   0.000    -.8789921    -.327131
          HI  |   .1473982   .2289974     0.64   0.520    -.3014284    .5962248
          IA  |  -.7473616   .1408955    -5.30   0.000    -1.023512   -.4712115
          ID  |  -1.320556   .1099344   -12.01   0.000    -1.536024   -1.105089
          IL  |  -1.686938   .1234972   -13.66   0.000    -1.928988   -1.444888
          IN  |  -1.925323   .1137321   -16.93   0.000    -2.148233   -1.702412
          KS  |  -1.846774   .1357318   -13.61   0.000    -2.112803   -1.580744
          KY  |  -1.947376   .1235094   -15.77   0.000     -2.18945   -1.705302
          LA  |  -1.417179   .1453278    -9.75   0.000    -1.702017   -1.132342
          MA  |  -1.969891   .1490648   -13.21   0.000    -2.262052   -1.677729
          MD  |  -1.382954   .1352641   -10.22   0.000    -1.648066   -1.117841
          ME  |  -.6129022   .1394874    -4.39   0.000    -.8862925   -.3395119
          MI  |  -1.680889   .1222681   -13.75   0.000     -1.92053   -1.441248
          MN  |  -.9551766   .1366952    -6.99   0.000    -1.223094   -.6872589
          MO  |  -1.070422   .1115914    -9.59   0.000    -1.289137   -.8517071
          MS  |          0  (empty)
          MT  |  -1.443551   .1808294    -7.98   0.000     -1.79797   -1.089132
          NC  |  -1.379022   .1037364   -13.29   0.000    -1.582342   -1.175703
          ND  |          0  (empty)
          NE  |  -.6262058   .1628795    -3.84   0.000    -.9454438   -.3069678
          NH  |   .0216396   .1375164     0.16   0.875    -.2478876    .2911668
          NJ  |  -1.109582   .1403572    -7.91   0.000    -1.384677   -.8344865
          NM  |  -1.766096   .1513496   -11.67   0.000    -2.062736   -1.469456
          NV  |  -2.239712    .120079   -18.65   0.000    -2.475063   -2.004362
          NY  |   -1.56185   .1486538   -10.51   0.000    -1.853206   -1.270494
          OH  |  -1.976724   .1023365   -19.32   0.000      -2.1773   -1.776148
          OK  |  -2.507271   .1318301   -19.02   0.000    -2.765654   -2.248889
          OR  |  -1.737527   .1495346   -11.62   0.000    -2.030609   -1.444444
          PA  |  -1.398963    .115927   -12.07   0.000    -1.626176    -1.17175
          RI  |          0  (empty)
          SC  |  -1.649453   .1100328   -14.99   0.000    -1.865114   -1.433793
          SD  |  -1.599923   .1051501   -15.22   0.000    -1.806014   -1.393833
          TN  |  -1.115365   .0959772   -11.62   0.000    -1.303477   -.9272537
          TX  |  -1.510336   .1121722   -13.46   0.000    -1.730189   -1.290482
          UT  |   -2.98252   .1143306   -26.09   0.000    -3.206604   -2.758436
          VA  |  -1.165322   .1265105    -9.21   0.000    -1.413278   -.9173661
          VT  |  -1.640765   .1598011   -10.27   0.000    -1.953969    -1.32756
          WA  |  -1.505657     .12683   -11.87   0.000    -1.754239   -1.257075
          WI  |  -1.361701    .109638   -12.42   0.000    -1.576588   -1.146815
          WV  |  -1.825403   .1170619   -15.59   0.000     -2.05484   -1.595965
          WY  |          0  (omitted)
              |
        _cons |  -2.188535   .3553511    -6.16   0.000     -2.88501    -1.49206
-------------------------------------------------------------------------------

. est sto hispchange

. 
. esttab foreign hispforeign hispchange using TableE8, replace tab label wrap va
> rwidth(25) cells("b(star fmt(2)) se(fmt(2) par)") onecell star(* .1 ** .05 ***
>  .01) scalars("N Observations" "r2_p Pseudo R-squared") title(Table E.8. Logit
>  Models of the Assumption that the Described Policy will Benefit Immigrants an
> d Not Native-Born Americans, with Alternate ZIP Code Demographic Interaction T
> erms.) nobaselevels eqlabels(none) interaction(" X ") substitute(=1 "" "Hispan
> ic/Latino population growth in ZIP code, 2012-17 (pct. points)" "Hispanic popu
> lation growth" "X \% foreign-born population" "X % foreign-born" "X \% foreign
> -born Hispanic population" "X % foreign-born Hispanic" "X Hispanic population 
> growth" "X Hispanic growth" \% %) order(2.treatgroup 3.treatgroup zipforeignpc
> t2017s 2.treatgroup#c.zipforeignpct2017s 3.treatgroup#c.zipforeignpct2017s zip
> hispforeignpct2017s 2.treatgroup#c.ziphispforeignpct2017s 3.treatgroup#c.ziphi
> spforeignpct2017s ziphispgrowth1217s 2.treatgroup#c.ziphispgrowth1217s 3.treat
> group#c.ziphispgrowth1217s immigscale) indicate("State fixed effects=*statenum
> " ) nomtitles collabels(none) addnotes("State-clustered standard errors in par
> entheses" "Population statistics measured in standard deviations" "* p<0.1, **
>  p<0.05, *** p<.01") nolz 
(output written to TableE8.txt)

. 
. *Table E.9 - Economic interactions 
. global econcontrols "black hispanic otherrace male ideo01 pid7 agecat notbornu
> s parents_notbornus sr2k"

. logit benefit_immigonly i.treatgroup##c.unempzip2017 medinczip2017 immigscale 
> $econcontrols, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1066.3554  
Iteration 1:   log pseudolikelihood = -1013.2016  
Iteration 2:   log pseudolikelihood = -1011.8911  
Iteration 3:   log pseudolikelihood =   -1011.89  
Iteration 4:   log pseudolikelihood =   -1011.89  

Logistic regression                             Number of obs     =      2,025
                                                Wald chi2(17)     =     231.62
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =   -1011.89               Pseudo R2         =     0.0511

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .0381842    .342503     0.11   0.911    -.6331093    .7094776
Fiscal thr..  |   .9342602   .4763547     1.96   0.050     .0006222    1.867898
              |
 unempzip2017 |   .0151905   .0284121     0.53   0.593    -.0404963    .0708772
              |
   treatgroup#|
           c. |
 unempzip2017 |
Cultural t..  |  -.0014533   .0427253    -0.03   0.973    -.0851933    .0822868
Fiscal thr..  |  -.0771506   .0589798    -1.31   0.191    -.1927489    .0384476
              |
medinczip2017 |   .0856687   .0306124     2.80   0.005     .0256696    .1456679
   immigscale |   .1279235   .2947033     0.43   0.664    -.4496844    .7055314
        black |  -.1089732   .2449982    -0.44   0.656    -.5891608    .3712144
     hispanic |  -.0925834   .1603125    -0.58   0.564    -.4067901    .2216233
    otherrace |  -.4516353    .199745    -2.26   0.024    -.8431284   -.0601422
         male |   .1822081   .1001084     1.82   0.069    -.0140008     .378417
       ideo01 |   .6448048   .3009623     2.14   0.032     .0549296     1.23468
         pid7 |   .0743838   .0383385     1.94   0.052    -.0007582    .1495259
       agecat |   .1281271   .0768836     1.67   0.096     -.022562    .2788161
    notbornus |   .4181736   .4067965     1.03   0.304    -.3791328     1.21548
parents_not~s |   .2455874   .1828623     1.34   0.179    -.1128161    .6039909
         sr2k |   .3608357   .2933304     1.23   0.219    -.2140812    .9357527
        _cons |   -3.41678   .3981202    -8.58   0.000    -4.197082   -2.636479
-------------------------------------------------------------------------------

. est sto unemp

. logit benefit_immigonly i.treatgroup##c.medinczip2017 unempzip2017 immigscale 
> $econcontrols, vce(cluster statenum) 

Iteration 0:   log pseudolikelihood = -1066.3554  
Iteration 1:   log pseudolikelihood = -1014.3734  
Iteration 2:   log pseudolikelihood = -1013.0199  
Iteration 3:   log pseudolikelihood = -1013.0185  
Iteration 4:   log pseudolikelihood = -1013.0185  

Logistic regression                             Number of obs     =      2,025
                                                Wald chi2(17)     =     230.73
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1013.0185               Pseudo R2         =     0.0500

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |  -.4411563   .3788827    -1.16   0.244    -1.183753      .30144
Fiscal thr..  |   .1518713   .3990578     0.38   0.704    -.6302676    .9340102
              |
medinczip2017 |   .0450509   .0420678     1.07   0.284    -.0374005    .1275022
              |
   treatgroup#|
           c. |
medinczip2017 |
Cultural t..  |    .074055   .0559241     1.32   0.185    -.0355542    .1836642
Fiscal thr..  |   .0437835   .0676899     0.65   0.518    -.0888863    .1764533
              |
 unempzip2017 |  -.0100937   .0194427    -0.52   0.604    -.0482006    .0280133
   immigscale |   .1642185   .3005992     0.55   0.585    -.4249451    .7533822
        black |  -.1153657   .2470803    -0.47   0.641    -.5996342    .3689029
     hispanic |  -.1094632   .1626767    -0.67   0.501    -.4283036    .2093773
    otherrace |  -.4469919   .1987977    -2.25   0.025    -.8366283   -.0573555
         male |   .1840338   .1001899     1.84   0.066    -.0123349    .3804024
       ideo01 |   .6308792   .3037337     2.08   0.038      .035572    1.226186
         pid7 |   .0747581   .0382182     1.96   0.050    -.0001482    .1496643
       agecat |   .1300156   .0769737     1.69   0.091    -.0208502    .2808813
    notbornus |   .4280153   .4070443     1.05   0.293     -.369777    1.225808
parents_not~s |   .2646233   .1809235     1.46   0.144    -.0899802    .6192269
         sr2k |   .3435371   .2940428     1.17   0.243    -.2327761    .9198504
        _cons |  -3.005226   .4819288    -6.24   0.000    -3.949789   -2.060663
-------------------------------------------------------------------------------

. est sto medinc

. logit benefit_immigonly i.treatgroup##c.incomecat medinczip2017 unempzip2017 i
> mmigscale $econcontrols, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1066.3554  
Iteration 1:   log pseudolikelihood = -1001.9988  
Iteration 2:   log pseudolikelihood =  -999.9157  
Iteration 3:   log pseudolikelihood = -999.91357  
Iteration 4:   log pseudolikelihood = -999.91357  

Logistic regression                             Number of obs     =      2,025
                                                Wald chi2(18)     =     212.06
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -999.91357               Pseudo R2         =     0.0623

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |     .00371    .282174     0.01   0.990    -.5493408    .5567609
Fiscal thr..  |  -.0458701   .2788143    -0.16   0.869     -.592336    .5005958
              |
    incomecat |    .055595   .0354553     1.57   0.117    -.0138962    .1250861
              |
   treatgroup#|
  c.incomecat |
Cultural t..  |   .0022535   .0440049     0.05   0.959    -.0839945    .0885016
Fiscal thr..  |   .0873215   .0453725     1.92   0.054    -.0016069      .17625
              |
medinczip2017 |   .0653495   .0310624     2.10   0.035     .0044683    .1262308
 unempzip2017 |  -.0084562   .0167016    -0.51   0.613    -.0411908    .0242783
   immigscale |   .2297867   .3068092     0.75   0.454    -.3715482    .8311216
        black |  -.0212825   .2510604    -0.08   0.932    -.5133519    .4707868
     hispanic |  -.0454986   .1491546    -0.31   0.760    -.3378364    .2468391
    otherrace |  -.4020771   .2040188    -1.97   0.049    -.8019465   -.0022077
         male |   .1392766   .1004275     1.39   0.165    -.0575577    .3361109
       ideo01 |   .7300398    .315017     2.32   0.020     .1126178    1.347462
         pid7 |   .0578025   .0394783     1.46   0.143    -.0195734    .1351785
       agecat |   .1363783   .0767768     1.78   0.076    -.0141015     .286858
    notbornus |   .4390117   .4209737     1.04   0.297    -.3860815    1.264105
parents_not~s |    .231483   .1896046     1.22   0.222    -.1401351    .6031011
         sr2k |   .3661067   .3089812     1.18   0.236    -.2394854    .9716987
        _cons |  -3.475659   .4798214    -7.24   0.000    -4.416092   -2.535226
-------------------------------------------------------------------------------

. est sto income

. logit benefit_immigonly i.treatgroup##c.educ medinczip2017 unempzip2017 immigs
> cale $econcontrols, vce(cluster statenum)

Iteration 0:   log pseudolikelihood = -1066.3554  
Iteration 1:   log pseudolikelihood = -1005.6873  
Iteration 2:   log pseudolikelihood = -1003.9246  
Iteration 3:   log pseudolikelihood = -1003.9221  
Iteration 4:   log pseudolikelihood = -1003.9221  

Logistic regression                             Number of obs     =      2,025
                                                Wald chi2(18)     =     224.20
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1003.9221               Pseudo R2         =     0.0585

                               (Std. Err. adjusted for 51 clusters in statenum)
-------------------------------------------------------------------------------
              |               Robust
benefit_imm~y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   treatgroup |
Cultural t..  |   .4530889    .392872     1.15   0.249    -.3169261    1.223104
Fiscal thr..  |   .4954971   .3589267     1.38   0.167    -.2079864    1.198981
              |
         educ |   .2776143   .0888033     3.13   0.002     .1035631    .4516655
              |
   treatgroup#|
       c.educ |
Cultural t..  |  -.1485711   .1234917    -1.20   0.229    -.3906105    .0934682
Fiscal thr..  |   -.026413   .1018986    -0.26   0.795    -.2261305    .1733045
              |
medinczip2017 |   .0728491   .0314072     2.32   0.020     .0112921    .1344061
 unempzip2017 |  -.0043278   .0180533    -0.24   0.811    -.0397116     .031056
   immigscale |   .2540983   .2988753     0.85   0.395    -.3316865    .8398832
        black |   -.061744   .2482121    -0.25   0.804    -.5482308    .4247428
     hispanic |  -.0300397   .1611377    -0.19   0.852    -.3458638    .2857844
    otherrace |  -.4494623   .2116947    -2.12   0.034    -.8643763   -.0345484
         male |   .1194817   .1057522     1.13   0.259    -.0877887    .3267522
       ideo01 |   .6727519   .3069406     2.19   0.028     .0711593    1.274344
         pid7 |    .061307    .038712     1.58   0.113    -.0145671    .1371811
       agecat |   .0924065   .0752967     1.23   0.220    -.0551723    .2399853
    notbornus |   .3360074   .4318908     0.78   0.437     -.510483    1.182498
parents_not~s |   .2069848   .1872979     1.11   0.269    -.1601124    .5740821
         sr2k |   .4617465   .2994124     1.54   0.123    -.1250909    1.048584
        _cons |  -3.970606    .536314    -7.40   0.000    -5.021762    -2.91945
-------------------------------------------------------------------------------

. est sto educ

. esttab unemp medinc income educ using TableE9, replace tab label wrap varwidth
> (25) cells("b(star fmt(2)) se(fmt(2) par)") onecell star(* .1 ** .05 *** .01) 
> scalars("N Observations" "r2_p Pseudo R-squared") title(Table E.9. Logit Model
> s of the Assumption that the Described Policy will Benefit Immigrants and Not 
> Native-Born Americans, with Economic Interaction Terms) nobaselevels eqlabels(
> none) interaction(" X ") substitute(=1 "" ) order(2.treatgroup 3.treatgroup un
> empzip2017 2.treatgroup#c.unempzip2017 3.treatgroup#c.unempzip2017 medinczip20
> 17 2.treatgroup#c.medinczip2017 3.treatgroup#c.medinczip2017 incomecat 2.treat
> group#c.incomecat 3.treatgroup#c.incomecat educ 2.treatgroup#c.educ 3.treatgro
> up#c.educ immigscale) indicate("Controls=black hispanic otherrace male ideo01 
> pid7 agecat notbornus parents_notbornus sr2k" ) nomtitles collabels(none) addn
> otes("State-clustered standard errors in parentheses" "* p<0.1, ** p<0.05, ***
>  p<.01") nolz
(output written to TableE9.txt)

. 
. *Table E.10 & Figure E.1 - Approval Model with Categorical Assumption Variable
. reg approval b4.whobencat##c.immigscale i.treatgroup, robust

Linear regression                               Number of obs     =      2,126
                                                F(9, 2116)        =      46.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1477
                                                Root MSE          =     1.7836

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    whobencat |
Immigrants..  |   .4997623   .2225176     2.25   0.025     .0633861    .9361384
Immigrants..  |   .8478968   .2179867     3.89   0.000     .4204061    1.275387
Born US only  |  -.0903064   .2856224    -0.32   0.752    -.6504364    .4698236
              |
   immigscale |  -.7170294   .2734132    -2.62   0.009    -1.253216   -.1808427
              |
    whobencat#|
 c.immigscale |
Immigrants..  |  -2.861712   .4070853    -7.03   0.000    -3.660041   -2.063383
Immigrants..  |  -.9186073   .4980562    -1.84   0.065    -1.895338    .0581236
Born US only  |   1.424707   .5291369     2.69   0.007     .3870241     2.46239
              |
   treatgroup |
Cultural t..  |   .0952696   .0941618     1.01   0.312    -.0893898    .2799289
Fiscal thr..  |   .1054179   .0954109     1.10   0.269     -.081691    .2925268
              |
        _cons |    4.96525   .1446719    34.32   0.000     4.681536    5.248964
-------------------------------------------------------------------------------

. est sto approvalcat

. reg approval b4.whobencat##c.immigscale i.treatgroup $controls, robust

Linear regression                               Number of obs     =      2,106
                                                F(21, 2084)       =      47.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2848
                                                Root MSE          =     1.6417

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    whobencat |
Immigrants..  |   .4273148   .2128312     2.01   0.045      .009931    .8446987
Immigrants..  |   .5453712   .2091662     2.61   0.009     .1351747    .9555677
Born US only  |  -.1322636   .2843122    -0.47   0.642    -.6898292    .4253019
              |
   immigscale |    .526891    .271223     1.94   0.052    -.0050053    1.058787
              |
    whobencat#|
 c.immigscale |
Immigrants..  |  -2.177164   .3816326    -5.70   0.000    -2.925585   -1.428744
Immigrants..  |   -.642357   .4568637    -1.41   0.160    -1.538314    .2535997
Born US only  |   1.212469   .5189248     2.34   0.020     .1948042    2.230134
              |
   treatgroup |
Cultural t..  |   .1025782   .0869592     1.18   0.238    -.0679579    .2731142
Fiscal thr..  |     .07162   .0887263     0.81   0.420    -.1023813    .2456213
              |
        black |  -.2320998   .1235926    -1.88   0.061    -.4744777    .0102781
     hispanic |  -.1324454   .1576519    -0.84   0.401    -.4416169    .1767262
    otherrace |   .0887335   .1582614     0.56   0.575    -.2216334    .3991005
         male |   .0372258   .0732645     0.51   0.611    -.1064535    .1809051
       ideo01 |  -1.178358   .1774118    -6.64   0.000    -1.526281   -.8304356
         pid7 |  -.1065777   .0231782    -4.60   0.000    -.1520326   -.0611228
       agecat |  -.1461339     .04141    -3.53   0.000    -.2273431   -.0649247
    incomecat |  -.1118865   .0134761    -8.30   0.000    -.1383146   -.0854585
         educ |  -.1214806   .0357668    -3.40   0.001    -.1916229   -.0513382
    notbornus |   .4567142   .2071705     2.20   0.028     .0504315    .8629968
parents_not~s |  -.2013257   .1148168    -1.75   0.080    -.4264932    .0238419
         sr2k |  -1.113015   .1923441    -5.79   0.000    -1.490222   -.7358089
        _cons |     7.2857   .2073491    35.14   0.000     6.879067    7.692333
-------------------------------------------------------------------------------

. est sto approvalcatcont

. lincom _b[1.whobencat#c.immigscale]-_b[2.whobencat#c.immigscale]

 ( 1)  1.whobencat#c.immigscale - 2.whobencat#c.immigscale = 0

------------------------------------------------------------------------------
    approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -1.534807   .4765032    -3.22   0.001    -2.469279   -.6003356
------------------------------------------------------------------------------

. margins, over(whobencat) at(immigscale=(0(.2)1))

Predictive margins                              Number of obs     =      2,106
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : whobencat

1._at        : 1.whobencat
                   immigscale      =           0
               2.whobencat
                   immigscale      =           0
               3.whobencat
                   immigscale      =           0
               4.whobencat
                   immigscale      =           0

2._at        : 1.whobencat
                   immigscale      =          .2
               2.whobencat
                   immigscale      =          .2
               3.whobencat
                   immigscale      =          .2
               4.whobencat
                   immigscale      =          .2

3._at        : 1.whobencat
                   immigscale      =          .4
               2.whobencat
                   immigscale      =          .4
               3.whobencat
                   immigscale      =          .4
               4.whobencat
                   immigscale      =          .4

4._at        : 1.whobencat
                   immigscale      =          .6
               2.whobencat
                   immigscale      =          .6
               3.whobencat
                   immigscale      =          .6
               4.whobencat
                   immigscale      =          .6

5._at        : 1.whobencat
                   immigscale      =          .8
               2.whobencat
                   immigscale      =          .8
               3.whobencat
                   immigscale      =          .8
               4.whobencat
                   immigscale      =          .8

6._at        : 1.whobencat
                   immigscale      =           1
               2.whobencat
                   immigscale      =           1
               3.whobencat
                   immigscale      =           1
               4.whobencat
                   immigscale      =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
_at#whobencat |
           1 #|
Immigrants..  |   4.482542   .1871686    23.95   0.000     4.115485    4.849599
           1 #|
Immigrants..  |   5.224603    .176759    29.56   0.000     4.877961    5.571246
           1 #|
Born US only  |   4.381173   .2597221    16.87   0.000     3.871831    4.890515
   1#Neither  |   4.406405   .1398363    31.51   0.000     4.132171    4.680638
           2 #|
Immigrants..  |   4.152487   .1307516    31.76   0.000      3.89607    4.408904
           2 #|
Immigrants..  |    5.20151   .1132151    45.94   0.000     4.979483    5.423537
           2 #|
Born US only  |   4.729045   .1769167    26.73   0.000     4.382093    5.075997
   2#Neither  |   4.511783   .0915307    49.29   0.000     4.332282    4.691284
           3 #|
Immigrants..  |   3.822432   .0859887    44.45   0.000       3.6538    3.991065
           3 #|
Immigrants..  |   5.178417   .0873806    59.26   0.000     5.007054    5.349779
           3 #|
Born US only  |   5.076917   .1121048    45.29   0.000     4.857068    5.296766
   3#Neither  |   4.617161   .0555562    83.11   0.000      4.50821    4.726112
           4 #|
Immigrants..  |   3.492378   .0769826    45.37   0.000     3.341407    3.643348
           4 #|
Immigrants..  |   5.155324   .1252871    41.15   0.000     4.909623    5.401024
           4 #|
Born US only  |   5.424789   .1061108    51.12   0.000     5.216695    5.632883
   4#Neither  |   4.722539   .0606635    77.85   0.000     4.603572    4.841507
           5 #|
Immigrants..  |   3.162323   .1126623    28.07   0.000     2.941381    3.383265
           5 #|
Immigrants..  |    5.13223   .1923593    26.68   0.000     4.754994    5.509467
           5 #|
Born US only  |   5.772661   .1654557    34.89   0.000     5.448186    6.097137
   5#Neither  |   4.827917   .1007898    47.90   0.000     4.630258    5.025577
           6 #|
Immigrants..  |   2.832268   .1664134    17.02   0.000     2.505914    3.158622
           6 #|
Immigrants..  |   5.109137   .2674904    19.10   0.000     4.584561    5.633713
           6 #|
Born US only  |   6.120533    .246811    24.80   0.000     5.636511    6.604555
   6#Neither  |   4.933296   .1500736    32.87   0.000     4.638986    5.227605
-------------------------------------------------------------------------------

. marginsplot, ytitle(Predicted policy approval) title("") recast(line) recastci
> (rarea) ci1opts(fcolor(%30)) ci2opts(fcolor(%30)) ci3opts(fcolor(%30)) ci4opts
> (fcolor(%30)) addplot(histogram immigscale, ylabel(0(1)7) legend(order(5 "Immi
> grants only" 6 "Both immigrants & born in US" 7 "Born in US only" 8 "Neither")
>  below)) scheme(s1mono) level(90) 

  Variables that uniquely identify margins: immigscale whobencat

. graph export FigureE1.png, as(png) replace
(file FigureE1.png written in PNG format)

. 
. esttab approvalcat approvalcatcont using TableE10, replace tab label wrap varw
> idth(25) b(%12.2g) se(%12.2g) star(* 0.1 ** 0.05 *** .01) scalars("N Observati
> ons" "r2 R-squared") title(Table E.10. Linear Regression Models of Policy Appr
> oval, with Categorical Assumption Interaction Terms and Robust Standard Errors
> ) nobaselevels eqlabels(none) interaction(" X ") substitute(=1 "" "Anti-immigr
> ation scale" "Anti-immigration" \& &) order(1.whobencat 2.whobencat 3.whobenca
> t immigscale 1.whobencat#c.immigscale 2.whobencat#c.immigscale 3.whobencat#c.i
> mmigscale 2.treatgroup 3.treatgroup ideo01 sr2k) indicate(Controls = black his
> panic otherrace male pid7 agecat incomecat educ notbornus parents_notbornus) n
> omtitles nolz
(output written to TableE10.txt)

. 
. *Table E.11 - Approval Model with Ideology & Symbolic Racism Interactions
. reg approval i.benefit_immigonly##c.immigscale ideo01 i.benefit_immigonly#c.id
> eo01 sr2k i.benefit_immigonly#c.sr2k i.treatgroup, robust 

Linear regression                               Number of obs     =      2,106
                                                F(9, 2096)        =      70.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2024
                                                Root MSE          =     1.7287

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_i~y |   .1570651   .2299441     0.68   0.495    -.2938775    .6080077
   immigscale |   .7374784   .2310911     3.19   0.001     .2842865     1.19067
              |
benefit_imm~y#|
 c.immigscale |
           1  |  -2.658403   .5063428    -5.25   0.000     -3.65139   -1.665416
              |
       ideo01 |  -1.606768   .1853122    -8.67   0.000    -1.970184   -1.243353
              |
benefit_imm~y#|
     c.ideo01 |
           1  |   .3512722    .409733     0.86   0.391    -.4522538    1.154798
              |
         sr2k |  -1.303864   .2118639    -6.15   0.000     -1.71935   -.8883788
              |
benefit_imm~y#|
       c.sr2k |
           1  |   -.000547   .4814421    -0.00   0.999    -.9447013    .9436074
              |
   treatgroup |
Cultural t..  |   .0513457   .0914961     0.56   0.575     -.128087    .2307784
Fiscal thr..  |   .0713722   .0931144     0.77   0.443     -.111234    .2539784
              |
        _cons |   5.969404   .1242566    48.04   0.000     5.725725    6.213084
-------------------------------------------------------------------------------

. est sto extraint

. reg approval i.benefit_immigonly##c.immigscale ideo01 i.benefit_immigonly#c.id
> eo01 sr2k i.benefit_immigonly#c.sr2k i.treatgroup black hispanic otherrace mal
> e pid7 agecat incomecat educ notbornus parents_notbornus, robust 

Linear regression                               Number of obs     =      2,106
                                                F(19, 2086)       =      49.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2736
                                                Root MSE          =     1.6538

-------------------------------------------------------------------------------
              |               Robust
     approval |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.benefit_i~y |   .2199473   .2280249     0.96   0.335    -.2272326    .6671273
   immigscale |   .6820166   .2300596     2.96   0.003     .2308462    1.133187
              |
benefit_imm~y#|
 c.immigscale |
           1  |  -2.484028   .4737271    -5.24   0.000    -3.413055   -1.555001
              |
       ideo01 |   -1.29499   .1925503    -6.73   0.000    -1.672601   -.9173793
              |
benefit_imm~y#|
     c.ideo01 |
           1  |   .3720503   .3985286     0.93   0.351    -.4095049    1.153605
              |
         sr2k |  -1.166246   .2131149    -5.47   0.000    -1.584186   -.7483059
              |
benefit_imm~y#|
       c.sr2k |
           1  |   .0619646   .4619064     0.13   0.893    -.8438808    .9678101
              |
   treatgroup |
Cultural t..  |   .0826662    .087695     0.94   0.346    -.0893127    .2546451
Fiscal thr..  |    .066651   .0893798     0.75   0.456     -.108632    .2419339
              |
        black |  -.2273768   .1230518    -1.85   0.065    -.4686938    .0139403
     hispanic |   -.133367    .158388    -0.84   0.400     -.443982    .1772479
    otherrace |   .0887863    .158115     0.56   0.574    -.2212932    .3988659
         male |   .0193911   .0739978     0.26   0.793    -.1257261    .1645084
         pid7 |   -.109523   .0234208    -4.68   0.000    -.1554535   -.0635925
       agecat |  -.1390084   .0415027    -3.35   0.001    -.2203994   -.0576173
    incomecat |  -.1153026      .0135    -8.54   0.000    -.1417774   -.0888277
         educ |  -.1266681   .0361711    -3.50   0.000    -.1976033   -.0557328
    notbornus |   .4034795   .2102077     1.92   0.055    -.0087592    .8157181
parents_not~s |  -.1974403   .1163311    -1.70   0.090    -.4255774    .0306969
        _cons |   7.473343   .1894688    39.44   0.000     7.101775     7.84491
-------------------------------------------------------------------------------

. est sto extraintcont

. 
. esttab extraint extraintcont using TableE11, replace tab label wrap varwidth(2
> 5) b(%12.2g) se(%12.2g) star(* 0.1 ** 0.05 *** .01) scalars("N Observations" "
> r2 R-squared") title(Table E.11. Linear Regression Models of Policy Approval, 
> with Additional Interaction Terms and Robust Standard Errors) nobaselevels eql
> abels(none) interaction(" X ") substitute(=1 "" "Anti-immigration scale" "Anti
> -immigration" "Symbolic racism scale" "Symbolic racism") order(1.benefit_immig
> only immigscale 1.benefit_immigonly#c.immigscale ideo01 1.benefit_immigonly#c.
> ideo01 sr2k 1.benefit_immigonly#c.sr2k 2.treatgroup 3.treatgroup) indicate(Con
> trols = black hispanic otherrace male pid7 agecat incomecat educ notbornus par
> ents_notbornus) nomtitles nolz
(output written to TableE11.txt)

. 
. log close
      name:  <unnamed>
       log:  /Users/haselswerdtj/Box Sync/Immigrants & Social Welfare/Replicatio
> n/immigsocwef_main_rep.log
  log type:  text
 closed on:   9 Jun 2020, 16:02:47
--------------------------------------------------------------------------------
